{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前版本信息:3.9.5 (default, Jun  4 2021, 12:28:51) \n",
      "[GCC 7.5.0]\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(f'当前版本信息:{sys.version}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、市场中位数统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "股票数：4842\n",
      "计算完成：\u001b[1;92m====================================================================================================>100%\u001b[0m\n",
      "中国股市自\u001b[1;92m2021-12-31\u001b[0m收盘至\u001b[1;92m2022-10-20\u001b[0m共获取到\u001b[1;31m4560\u001b[0m支股票的数据，开始分析...\n",
      "##################################################\n",
      "涨幅最高的是：\u001b[1;31mST实达\u001b[0m\n",
      "涨幅最低的是：\u001b[1;92m*ST紫晶\u001b[0m\n",
      "自\u001b[1;92m2021-12-31\u001b[0m收盘至\u001b[1;92m2022-10-20\u001b[0m，你需要收益率达到\u001b[1;31m-6.15%\u001b[0m才能超越70.00%的股票！\n",
      "如果你是红的就超过了\u001b[1;31m77.06%\u001b[0m的股票！\n"
     ]
    },
    {
     "data": {
      "image/png": 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rK/H7/QNMYYwxxzdLdimourqanzy4hJKxE2mq385NV8KMGTMSHZYxxiQtS3YpqmTsRMomTkl0GMYYkxLimuxE5DzcU4hzcHfl/xTuZrh/x90Ed56q3uaNewtwDe6mt1ep6pZ4xpYuQqEgNTU1B/Wzak1jjDlYvEt2m4BLVbVRRD6FexyKH3en+EeBl7ynB3cAVwAzgLm4R658JM6xpYXmhnrurW5lcr178KRVaxpjzKHimuzUe2ih95DDmcAa4Ebg86oa9B5tcSHu2VcLvX4LgV/GM650U1w2zqo0jTFmAHE/ZyciXwT+G/cE4VuAb6pq+GnADcA4XMmuEUBV+7ynCdujMAbQ3qus3bCb7R3FBDsD7K5uZPro/EOqNa1K0xhjhiHZqepdIvIL3NOhf4V7UnIk6affIUTkBuAGgPLy8mMbaIoIhZT5K5p4YksIkVZG4MPvgxXbmqna2szkzDY2V+9gar1YlaYxxniGpTWmqqqI/Bn3xOA2Ecn2SndluNJdC66aM/zkYI1VqlPVe4B7AE4//fTj7kF8vcEQN/3tDR5b1UxFvvDOkydRu/pl/NkjKD/hFJZubOStetibUckZ4ysSHa4xxiSNuN5BRUSme0/yBbgYeBN4GrhURPzAZcAz3usSr99cYGk840o1wWCQdevWcd3vlvLYG3VcPlk4e4yQn52xf5zsDD/vmTWaWdktNPUGeGrNLuzBvMYY48S7ZHcJ8HkR2QfsxFVBdgGPA3cAf1DVdQAiMg9Yjzt/d1mc40o60XdFgQPn26qrq7nxL6+zsaeAOaVC3doVlJRXMjrGfCZk7iPoz2Z9A2T1xawNNsaY4068W2PeBdwVY9DZMca9G7g7nvEks8i7osDBlxCsrOukuqeA6WV5XHjiGNb11A04r8m5PfgKRrGmvo31u7uwU3bGmOOd3Qg6iYTvilI2ccr+pNfR3cdPX2wgLwPeNXM07iqOgYnAhdNHkROAO5buoqvXGrUaY45vluyS3I+fWs+u9j7OGuMjMzD43ZUV8HNGGWxv7eWHDy9j/fr1rF+/nmDQEp8x5vhj98ZMYhsbu5j/Ui2XnVBIXqh9yNNntddTTB73rYS9LS107K61SxGMMcclK9klKVXlnuWNjMzN5FOnjTzi+Zw4UggqbOrO3181aowxxxsr2SWpug5YtbOLWz84mxGZ3Uc8n7xAiJPGFbK6rpWJ5djdVYwxxyVLdkkopMrK3SHKcuC0oi5qajZzNJfMnTF5JGvq9vJGXTtNW7cz2e6uYow5zliyS0Ibd7WztxdG9+7gzy/72bSqqt/r6gYjLyvAzLH5vFUXYnrJeLtptDHmuGPn7JKMqrJ86x5G+HqZOiqfsolTKCobe9TzPW1SMSFgy77Mow/SGGNSjCW7JLO5sYOm9h4mZ3YwiEvqBq0oN5PRgS627cukpy907GZsjDEpwJJdkqna2kxBdoAxGfuO+bzLMzvoU2H9zrZjPm9jjElmluySSFOXUt/axanlxfjicFvLIn8v+YEgb+xosZtEG2OOK5bsksiGFiXDL8wcmx+X+YvApJwemtp72H3sC47GGJO0LNkliZauINvalJljCsgKxO/at3HZvWQFfGxstZKdMeb4YckuSTy5oZWQwpyJRXFdjl9g1tgCtrcpTZ19cV2WMcYkC0t2SUBVeXJDG2U5MHJE/C8NOGlCIQosXL837ssyxphkYMkuCSzbvIf6tl6mFA7Pw1aLczMZmwtPrG+lN2iXIRhj0p8luyTwt6pacjOEiXnD92TxaUU+9uwLsmjNzmFbpjHGJIoluwRr7+5jwep6LpicTyAe1xv0Y+wIGJMX4I8vbR22ZRpjTKJYskuwBavr2dcb5L2V8bncoD8+ES6bWciyLXtYU9c6rMs2xpjhZskuwR5bWcekklxmlWUP+7IvnlZAToafe1/aMuzLNsaY4RTXZCciE0RkgYi8JSJLRGSciEwRkRUiUi0iN0eMe4vXb7mIVMQzrmTR0NbFS5sauezkccixvBHmIOVn+bnibeN5dGUdezp6hn35xhgzXOJdsusGfqSqs4C/A18Gvg3cBswAPiAis0RkEnCF1+9W4PY4x5VwwWCQ+c+8QUjhpMJuampqEnILr2vOqaC7L8QDy7cN+7KNMWa4DCrZicj3YvT7/uGmU9XdqrrE69wCFAMXAgtUNQj80+u+CFjo9VsInDOo6FNYdXU19y2vozATFr9Vx7xFVbS2tgx7HDPG5HPu1BL+/PJW+uwyBGNMmhow2YlIpYhcCnxMROZGvD4CfGyIy7oKWASMUNUur18DMAYoAxoBVLUP8ItI/O6ZlQR2tffSGspk9sSSY/bMuiN1zbkV1LV28czaXQmLwRhj4ulwTyr3AecDhbhkFdYBXDfYhYjIXKAceBC4K3owEF1/F/MElojcANwAUF5ePtjFJ41gMEh1dTUAj65w1YbTyvISGRIA7545mvFFOcx7cQuXnJi4pGuMMfEyYLJT1Q3AN0XkEVV95UgWICKVwI+Ai1VVRaRNRLK90l0ZrnTXAsz0xg+4RWswRjz3APcAnH766Sl3J+Pq6mp+8uASSsZOZFF1G/mZORTlJv7J4X6fcO25Fdy2YC2vb2vm5PEF+5NyWGVlJX5/Whe2jTFpbLANVHwi8oCIPCsiz4Vfh5tIRPKB+4FrVLXe6/00cKlXTXkZ8Iz3usTrNxdYOuQ1SRElYycyoqycdt8IxmT1Jjqc/T5+VjnFuRn84rnq/Ul5/oubmf/iZn7y4JJDkp8xxqSSw1Vjhv0B+DqwaYjz/wJQCczzmtZ3Ah8EHgfuAP6gqusARGQesB5XRXrZEJeTUqp3twPC6KzkeerAiKwA158/hTsWredD0zIpGTuRsolTEh2WMcYcE4NNdu2q+thQZ66qPwR+GGPQ2THGvRu4e6jLSEWbGtrJ9fWR509c68dQKEhNTc1B/a4+s4LfPr+J+95opmL4r3E3xpi4GWyye0lEHgeew107B4Cq/iouUaWx7qBS27KPiowuEnAd+X7NDfXcW93K5HoXRFP9dm66Eq47bwp3PrOBwnIfZYkLzxhjjqnBJrsm71UQx1iOC7XtiiqMztgHZCU0luKycYdUVV779gruWVLNm3tCzEhQXMYYc6wNKtmp6v/GO5DjRW27UpAdIN/XR6KTXSyFORlcPquQ+1Y209jeTWle8sVojDFDNdg7qPwrshWm93o23sGlm46eEDs7YWpZXkKrMA/n8llFBHzwSk1TokMxxphjYrDVmF+I+JwDvAsY3mfSpIFXt3cQUqgclUcC7gw2oMgGK407tnJCkfDm7g7qWvYN+ktijDHJarDVmGuielWJyErcTZ3NIL24tZ0cP4wtzCbZniAX2WBl06oqxk6spCazmBeqG7mwLOWu3zfGmIMMKtmJyOejek0FkueK6BSwryfI8h2dlOdJQh7nMxjhBitNO2vxC5w9pYTn1jWwY4Q99tAYk9oG+y82KuJViru4/H3xCiodPb+hge4+ZUJ+cia6WGaPLaA4N4OVu0P0hax0Z4xJXUNqjSkiY7zunfEMKh0tfHMnBVk+ynISHcng+XzCeZWlPL6qnic37GX2zERHZIwxR2awrTHPFpE1wKPAYyLypoicG9/Q0kd3X5Dn1jZwTvkIfElahdmfyaUjGJUDf165h47u5Lm9mTHGDMVgqzF/jntqwVmqeibwXuDO+IWVXl6qbqKtu4+3T0r843yGSkQ4pdRH874gv1tac/gJjDEmCQ022WWqam1Edz3JeEV0klqwup78rACnjstNdChHpDRHOG/SCO5ZUsPutu7DT2CMMUlmsMnuIRF5SkS+KiJfwz1x/P44xpU2uvuCPLlmJ++ZPZpMf2pVYUb61Gkl9PSF+NkzGxIdijHGDNmAyU5E/lNEClT1e8AtuMsNunHX11md1iAs2dBIW1cfl80Zl+hQjsqEwkw+flY5DyzfzvqdbYkOxxhjhuRwJbsvqOpeAFV9VVV/rqp3qeoy4H/iH15qCwaD3PfCOvKzfJSF9lBTU4Nq6jXhD99d5bIKH7kZwtceWE5fnzVWMcakjsMlO/8Aw+yc3WG8uXYDS2r2Upat/PnlLcxbVEVrst0nbBCaG+q59/m3eGjFNmYUKqt27mPeM28kOixjjBm0wyW710TkuuieInIFsD0+IaWPV2s7COFjzhR3Z5KisrGJDumIhe+ucs6JUynKgnuWN9LZY6U7Y0xqOFyy+wzwYRGpEpHfisivRGQp8C3gk/EPL7U9X9NOth/GF6fQleSH4RPh9DIfuzv6+PXiTYkOxxhjBmXAO6ioaitwsYicAMwABPi1qq4ejuBS2d6uXpbt6GRyvqTcheSHMypHeOeUPH67pIYrT5vApJIRiQ7JGGMGNKhLD1R1nao+qqqPWKIbnKfX7KI3qExKoXthDsX1Z5SS4RO+/cibKdnoxhhzfIn77exFZKSILBWR73rdU0RkhYhUi8jNEePd4vVbLiIV8Y4r3h5fVcfovAAl2YmOJD5KcgP899yZLN3YyP3L7PStMSa5xTXZiUgGsBCIvBL528BtuGrRD4jILBGZBFzh9bsVuD2eccXbno4eXtjYyAUVeUn7OJ9j4eqzyjl3agm3PfEWtc2diQ7HGGP6Fddkp6q9wGXA0ojeFwILVDUI/NPrvghY6PVbCJwTz7ji7aHXaukLKe+uTM+HuYevu9uwYQOfnpNDKBTixj+9wrp161i/fj3BYDDRIRpjzEEG9Yifo6GqDVGlmxGq2uV9bgDGAR1Aozd+n4j4RcTvJb+U0tfXx59e3MSM0ixCzTtIx9NZBz/VfDljssfxet0YbllQQ/G+Hdx0JcyYMSPRYRpjzH6JeAR19N+/9NPvECJyg3cZRNXu3bvjEtzReuKVNWxt6aHA15OyF5EPRvi6u6KyscwYlcuE4hxWNkFmyYREh2aMMYdIRLJrE5Fws40yXOmuHvcEdEQkAGisUp2q3qOqp6vq6aNGjRq2gIfiyQ178QucNnNySl9EPhQi8J5Zo/GJ8GJ9iJ5gGhZnjTEpLRHJ7mngUhHx487nPeO9LvH6zeXgc3wpY29XL4tr2ijPF7ICA91pLf0UZGfw3lmjae6G3y1vTHQ4xhhzkLifs4vhu8DjwB3AH1R1HYCIzAPW487fXZaAuI7ag1W17OtTphUl4hgi8aaMymN6ITy2tpWTF63g/Io8Kisr8fuPr8RvjEk+w5LsVHV+xOfdwNkxxrkbuHs44omHUEi59+UtzCrLpiS7N9HhJEx5qJ4dFHH78ztZvGwXt3zYGqsYYxLv+CyCxMHiDQ1sberkgzMLEx1KQvkETi/pQ3w+3uwbzb7eUKJDMsYYS3bHyu+WbGZ0QRbnVeQlOpSEy/Ur7ztxDK3dcPuSXYRC1mDFGJNYluyOgRVbm3m5ponrz5tCwJe+d0wZioqSEZw6Snh5Wwe3L1qX6HCMMcc5S3bHwC//VU1xbgYfP6s80aEklelFwqUzCvjt8zX8rcrun2mMSZxEtMZMK6u2N/PcugauedtIardsoqamJi3vmnIkRITPnz2KlmAGNz+8mtEF2Vw4PTmvjzTGpDcr2R2l7z+6kgAh2ltbmP/i5rS+a8qRCPiEX119GtPK8rnhj1W8vKkp0SEZY45DluyOwkubGllW28nsUj8TKqbuv32WOVhhTgZ/uu5Mykfmcu28V7nvudfshtHGmGFl1ZhHKBRSfrRwHaW5AaYXWfP6WMJPRwi7fmoH32/s5pan6pmd3cyPP2bX4BljhocluyP08Os7WFXbyk3nlVHXYFVzsUQ+HQFg06oVnDWhkrV9haxugXtfa+L70xSftWA1xsSZVWMegaa2fdz6+JvMHJXFFN9u1Fqk9Cv8dIRwFW+GDy4/dRxTCoS/vNHMtfOXs7utO9FhGmPSnCW7I3DL36vY29XHpNxe7n1qhTVIGaKAz8eZo4UvnjOKV2uauORnS1iwut4OGowxcWPJbohe2NjIgg17mV7sY0blVGuQcoRUQ8zMbOKu94+nOEv4/H2vcd385dTsbk90aMaYNGTn7IagpbOHm/6+komFGZxcYi0Jj8b+83nThdNKFGlv4cVqeM9Pn+fi6QV8Ze4cKkcXJDpMY0yasJLdIKkq3/zHaprae/jGBaPttmDHQPh83pjyqUzO3sfs4EamFAgL17fynjuX8uk/VvFqTZNVbxpjjpqV7AYhGAxy64PLeHLNHj59Rgn+vXV2l5Q4GF02mvNPqmTr5k3k5heycOMenn5rF5NLczl3QhbvmJxPeVEmgD0nzxgzJJbsBmHeMyu59/U9VOQLbS3NzFtaRUl5JaMTHViayvKFOL+olY/+WwWLa9p5Yk0D963s4L6VzRRmQjFtfPaiTi4/72SyMw5OeMFgkOrq6v3dlhSNMWDJ7rCeXbuLHz2/k5JsuPS0KQT8Pvbs2pHosNJa5Pk8gMKdr3HR+GkER05m0+4OtrXANxfV8d3ndnLyhCJOGl/ISeMLOXF8AT1NO/jZQ0spGTuRpvrt3HSlXbhujLFkN6AFq+v50gMrmTIyi5MKewn47RTncAmfzwNo2lmL369MLy/m1PJitm/eyJhcoS6Yz9rdHfzp5WZ6gq5e2S+QlzGO0S05ZGWOZ+mWdvzFbUwqGUGG7T9jjluW7GLoC4a469mN3PVcNaeWF3HzecX8o2pbosMynvbGel7b28rk6bMprF3Oqdm5jJw0i+ZuZWttPX0ZJexq66Z1n7L6Xzv5/r92EvDBxMJM5kwqZda4Ak4YU8DMsQWMys9K9OoYY4aBJbsoq2tb+dbDq1m9o5X3TsvnxnNKqN262RqkJJlwya9pZy3+7BFMr5wKQEFnHf7sfUw/aRZvLltCQ1sXI8ZMobUbGva2svitHh5ZWbd/PkXZfiaPzGRCQSazK8ZQUTqCcYXZ9DbXk5d14FyfnfszJrUlVbITkVuAa4Bm4CpV3TIcyw2FlFc2N/GHFzbzzNoGSvOy+NY7RrO86nX+8konm1ZZg5RU5BeYUFbC9NmVAKxdvoS9e7cybuos3lq7jn2Zhfgyx7C1aR+r6zp4fF3rQdNn+CDbD4FQNydN3MmUcaWMys+iKDeD4txMinIzKMjy09JQR0GWj6yAqya1xGhM8kmaZCcik4ArgBnAXOB24CPxWl5LRxcPvbiG1+o6eWVbB42dQQqyfHzy1JFcPrOQhh1bqRk7YX/pwaSH4rJxTKyYSvvuHfizc5h+0oFEuGdvGyXlJ7Cpuhp/8ThyisfQ0dNHy15Yt6udqh2ddPb2/4QLv0CAPkpzqynJz6EgSyjK9lOcm0Fxtp+iHD+zp04iLyeDLL+wa8d2Mv2CT8DnE6ZNnUpmRgC/z/UTsWs5jTlWkibZARcBC1U1KCILgV/Gc2H/eGEN3/vXTgICo3Nhas9mRtJDX8csHqxqtdLccWhU2RimT5uKtuzAn93D9BnuqequROjOEW5c9Tqh7HzKJk2nJwjbaqrJLhlLQdkEunqD7Kqvo7NjHz0h2NDRRa8vgyARpbzndg4QQc1BXT5xCdQnLvmFk2CGX8j0Cxl+Hxk+yPQLmX4fGX4hw+eGZwVc96iRxQT8PoIhpS8YYk9LC6EQ9IVChBQUCCkEQ0pIFb/PR0AUv8/NP+ATAj4oLRlJVsCPX6C1ZY/XX/ChBLzlu2mECePGkpURcNP7XT83L9+Bz+H4vc9+EUKqKKDqbuKg++MLf/bevc+hqFMLkYcG4eME8fpGHjdIxIfI4eH+oVCILZs37+8hwNQpU/D5/fvHCx+IyP5pYyzH6y8R8xERBnsI47aFRnw+sA32jxO5Xfb3O7Adwz3V+yDi4gl/p3wR3cKB7mNxoLU/9qjYItcnMraAz32H4iWZkl0Z0Aigqn0i4hcRv6rG5b5cbxufyxk5jUyeUIZfhE1NzfjIPWic5oY6GrYX09JQjy87l4btxQAHdcdj2HAsI1VjS+TyAffnTh/FWe5H2R5qwdfWw6SxeeCHQNs6fNm5TJ44m02r3sKXnUv5tFl098GuXbs4Y2oZRaWj2V6/i2Wbm8kuKEaBxrrtdHV3k19cQmtjAwQyyC8ciQJ7m5vAn8GIgiLaW/bQ588gI6+QLoSm9jbUn0Fmdi4hhO6ubtQXwJeRSV8wREbGXhDB7xM0FKK7pwe/30ewtxcRCAQCCNDX0wUiBDKy6O3tQcWPz5+BAkEFv7+VoHJIgoltoISeqjYnOoBhdSCJu4MuiE5QB4tMaEfq99eczrtmxq94kUzJLnpbHZLiReQG4Aavs11E1sc9qqEpxUvYacLW5xj767GdXcLX5xhLp/VJp3WBYVifd99+TGYzqb8ByZTs6oGZACISADS6VKeq9wD3JCC2QRGRKlU9PdFxHCu2PsnN1id5pdO6QHqsTzJdZfsMcImI+HENVJYmOB5jjDFpImlKdqq6U0TmAeuBDuCyBIdkjDEmTSRNsgNQ1buBuxMdx1FI2irWI2Trk9xsfZJXOq0LpMH6iD0rzBhjTLpLpnN2xhhjTFxYsjtCInKpiKz3Xu+LMfw+EVkrIiu9c5FJTURuEZFqEVkuIhVRw64TkY0i8qaInJagEIfkMOuz1dsvK0XkPxMU4pCIyEgRWSoi340xLBX3z0DrkzL7R0QmiMgCEXlLRJaIyLio4Sm1bwaxPimzbw7h7lRgr6G+gDXAeGAssAHwRQ1/EihPdJyDXJdJwGuAH9cw6K8Rw3KAjUAecBLwSqLjPZr1Ce+7RMc4xPXJAF4Ffg98N2pYKu6fftcn1fYPMAq4wPt8I3BHiu+bftcn1fZN9MtKdkfAKym0qOoOVa0HtuPu6RlpJKlzUen+W7UBC4FzIoadBSxT1XZVXQ2UikhurJkkkX7Xx4u9I1GBHQlV7cUl7ViX46Tc/hlofVJt/6jqblVd4nVuAYojBqfivul3fVJt30SzZHdk9t/azNMAjIkaJwAsFpE3ROTqYYvsyBx0qzbA713veNAwz25I+luGDrQ+uUCFiLzuVaMlfdUSgKo29DMoFffPQOuTkvvHcxWwKKI7JfdNhOj1SeV9k1yXHqSQWE1Yo29vdpqqqoiMB14QkcWqumMYYjsSA92qbTDrmmz6XR9VbRSR0d6+uQi4FzhxWKM7tlJx//QrVfePiMwFyoEHI3qn7L6JtT6pum/CLNkNgYjcjDvaacTVx4eV4Up3+6lXwa2qO0RkGTANSNZkN9Ct2upx98ULG4U7Qk1mA956LmLf/EtESuJ5w/FhkIr7Z0Cptn9EpBL4EXBxOHZPSu6bAdYn5fZNJKvGHAJVvU1VT1HVdwPZIjJeRMYCE4E1IvJhEblZnDEAIlIAzAHWJjD0wznkVm0icoeIvBdYBpwqInkiMgfYraptiQx2EPpdHxEpFpFsAG996lPlxxopxffPIVJ1/4hIPnA/cI13/j6l981A65Nq+yaaleyO3I3Ac7gWf1/wivajgAm4qopHRCQHV5XxfVXdlbhQB6axb9X2Y2CkqvaIyLeB14EQ8NHERTo4A60PrgXt/SLSB3QC1ycs0KMziRTdP/2YRGruny8AlcA8cc+A6wRqSd190+/6kHr75iB2BxVjjDFpz6oxjTHGpD1LdsYYY9KeJTtjjDFpz5KdMcaYtGfJzhhjTNqzZGeMMSbtWbIzJsWIyGgRuSPRcRiTSuw6O2OMMWnPSnbGpAARqRCRx0TkIRG5X0SqvP7Xi8gqcQ8Kvtbr9xkRWSEiVSJyZkIDNyZJ2O3CjEkdF+Pus9rFgbvRfwl4J+4Gw7nek6U/BJwBzAZ+BZw/7JEak2SsZGdM6tioquui+t0LPIR7unQHcCZwKu5J7X8CSoY3RGOSk5XsjEkdXdE9VPUOEXkKuFtE/gzsAh5W1c8Me3TGJDEr2RmTwkSkRFXfAL4PnAssB84Xkbzw8ETGZ0yysJKdMantYREpBPqAz3kPC/4B8IK4Z7Q8CXwjoREakwTs0gNjjDFpz6oxjTHGpD1LdsYYY9KeJTtjjDFpz5KdMcaYtGfJzhhjTNqzZGeMMSbtWbIzxhiT9izZGWOMSXuW7IwxxqS9/w/RTuXTqT74bwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x129.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "下跌\u001b[1;92m3514\u001b[0m家，占比：77.06%\n",
      "上涨\u001b[1;31m1041\u001b[0m家，占比：22.83%\n",
      "平盘\u001b[1;94m5\u001b[0m家，占比：0.11%\n",
      "中位数：\u001b[1;31m-17.78%\u001b[0m\n",
      "平均数：\u001b[1;31m-12.92%\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4560.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.129230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.289242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.691818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0%</th>\n",
       "      <td>-0.691818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5%</th>\n",
       "      <td>-0.464228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>-0.405608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15%</th>\n",
       "      <td>-0.367979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>-0.335891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.309249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30%</th>\n",
       "      <td>-0.278791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35%</th>\n",
       "      <td>-0.251885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40%</th>\n",
       "      <td>-0.226886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45%</th>\n",
       "      <td>-0.202758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.177814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55%</th>\n",
       "      <td>-0.151776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60%</th>\n",
       "      <td>-0.122221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65%</th>\n",
       "      <td>-0.097147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70%</th>\n",
       "      <td>-0.061512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.018386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>0.025456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85%</th>\n",
       "      <td>0.088131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>0.171124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>0.364058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.734216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              rise\n",
       "count  4560.000000\n",
       "mean     -0.129230\n",
       "std       0.289242\n",
       "min      -0.691818\n",
       "0%       -0.691818\n",
       "5%       -0.464228\n",
       "10%      -0.405608\n",
       "15%      -0.367979\n",
       "20%      -0.335891\n",
       "25%      -0.309249\n",
       "30%      -0.278791\n",
       "35%      -0.251885\n",
       "40%      -0.226886\n",
       "45%      -0.202758\n",
       "50%      -0.177814\n",
       "55%      -0.151776\n",
       "60%      -0.122221\n",
       "65%      -0.097147\n",
       "70%      -0.061512\n",
       "75%      -0.018386\n",
       "80%       0.025456\n",
       "85%       0.088131\n",
       "90%       0.171124\n",
       "95%       0.364058\n",
       "max       2.734216"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "from text import *\n",
    "\n",
    "today = datetime.date.today()\n",
    "year = today.year\n",
    "if today.weekday() > 4:\n",
    "    n = 2\n",
    "else:\n",
    "    n = 1\n",
    "start = f\"{today.year-1}-12-31\"\n",
    "# start='2022-4-27'\n",
    "today = f\"{today.year}-{today.month}-{today.day}\"\n",
    "yesterday = get_previous_trading_date(today, n, market=\"cn\")\n",
    "if datetime.datetime.now().hour < 16:\n",
    "    today = yesterday\n",
    "\n",
    "\n",
    "file_name = f\"csv/{year}中位数.csv\"\n",
    "if os.path.isfile(file_name):\n",
    "    df = pd.read_csv(file_name)\n",
    "    mids = list(df.涨幅中位数)\n",
    "    dates = list(df.日期)\n",
    "    datas = [(dates[i], mids[i]) for i in range(len(df))]\n",
    "else:\n",
    "    datas = []\n",
    "\n",
    "\n",
    "def mykey(i):\n",
    "    \"\"\"排序的函数\"\"\"\n",
    "    date = str(i[0])\n",
    "    date = datetime.datetime.strptime(date, \"%Y-%m-%d\")\n",
    "    return date\n",
    "\n",
    "\n",
    "def getStocks():\n",
    "    \"获取股票数据\"\n",
    "    df = all_instruments(type=\"CS\", market=\"cn\", date=today)\n",
    "    index = df.order_book_id\n",
    "    df = get_price(\n",
    "        index,\n",
    "        start_date=start,\n",
    "        end_date=today,\n",
    "        frequency=\"1d\",\n",
    "        adjust_type=\"pre\",\n",
    "        skip_suspended=False,\n",
    "        market=\"cn\",\n",
    "        fields=\"close\",\n",
    "        expect_df=False,\n",
    "    )\n",
    "    print(f\"股票数：{len(index)}\")\n",
    "    stocks = []\n",
    "    maxS = [0, 0]\n",
    "    minS = [0, 0]\n",
    "    for code in df.columns:\n",
    "        a = df[code][0]\n",
    "        b = df[code][-1]\n",
    "        if a > 0 and b > 0:\n",
    "            rise = b / a - 1\n",
    "            stocks.append([code, rise])\n",
    "            # name=get\n",
    "            if rise > maxS[-1]:\n",
    "                maxS = [code,  rise]\n",
    "            if rise < minS[-1]:\n",
    "                minS = [code,  rise]\n",
    "\n",
    "    print(f\"计算完成：{OKGREEN}\" + \"=\" * 100 + f\">100%{ENDL}\")\n",
    "    print(\n",
    "        f\"中国股市自{OKGREEN}{start}{ENDL}收盘至{OKGREEN}{today}{ENDL}共获取到{OKRED}{len(stocks)}{ENDL}支股票的数据，开始分析...\"\n",
    "    )\n",
    "    print(\"#\" * 50)\n",
    "    print(f\"涨幅最高的是：{OKRED}{instruments(maxS[0]).symbol}{ENDC}\")\n",
    "    print(f\"涨幅最低的是：{OKGREEN}{instruments(minS[0]).symbol}{ENDC}\")\n",
    "    return stocks\n",
    "\n",
    "\n",
    "def drawPic(stocks):\n",
    "    \"绘制图表\"\n",
    "    rises = [i[-1] for i in stocks]\n",
    "    ratio = 0.7\n",
    "    need = len(stocks) * ratio\n",
    "    need = int(need)\n",
    "    newRises = sorted(rises)\n",
    "    print(\n",
    "        f\"自{OKGREEN}{start}{ENDL}收盘至{OKGREEN}{today}{ENDL}，你需要收益率达到{OKRED}{newRises[need]*100:.2f}%{ENDC}才能超越{ratio*100:.2f}%的股票！\"\n",
    "    )\n",
    "    c = 0\n",
    "    d = 0\n",
    "    for rise in rises:\n",
    "        if rise < 0:\n",
    "            c += 1\n",
    "        if rise > 0:\n",
    "            d += 1\n",
    "    print(f\"如果你是红的就超过了{OKRED}{(c/len(newRises))*100:.2f}%{ENDC}的股票！\")\n",
    "    data = {\n",
    "        \"index\": [i[0] for i in stocks],\n",
    "        \"rise\": [i[1] for i in stocks],\n",
    "    }\n",
    "    df = pd.DataFrame(data=data)\n",
    "    fig = sns.histplot(df[\"rise\"], bins=100, kde=True)\n",
    "    # plt.gcf().set_size_inches(8, 2)#同时设置宽度和高度\n",
    "    fig.get_figure().set_figwidth(7)  # 设置宽度\n",
    "    fig.get_figure().set_figheight(1.8)  # 设置高度\n",
    "    plt.title(f\"a股自{start}收盘至{today}涨跌幅度分布\")\n",
    "    plt.show()\n",
    "    print(f\"下跌{OKGREEN}{c}{ENDC}家，占比：{c/len(rises)*100:.2f}%\")\n",
    "    print(f\"上涨{OKRED}{d}{ENDC}家，占比：{(d/len(rises))*100:.2f}%\")\n",
    "    print(f\"平盘{OKBLUE}{len(rises)-c-d}{ENDC}家，占比：{100-((c+d)/len(rises))*100:.2f}%\")\n",
    "    mid = round(newRises[len(newRises) // 2] * 100, 2)\n",
    "    ave = round(sum(newRises) / len(newRises) * 100, 2)\n",
    "    print(f\"中位数：{OKRED}{mid}%{ENDL}\")\n",
    "    print(f\"平均数：{OKRED}{ave}%{ENDL}\")\n",
    "\n",
    "    # 分成10档显示中位数统计\n",
    "    display(df[[\"rise\"]].describe(percentiles=[i * 0.05 for i in range(20)]))\n",
    "\n",
    "    dates = [str(i[0]) for i in datas]\n",
    "    if not str(today) in dates:\n",
    "        datas.append((today, mid))\n",
    "    dates = [i[0] for i in datas]\n",
    "    datas.sort(key=mykey)  # 按日期排序\n",
    "    data = {\n",
    "        \"日期\": pd.to_datetime([i[0] for i in datas]),\n",
    "        \"涨幅中位数\": [i[-1] for i in datas],\n",
    "    }\n",
    "    prices = []\n",
    "    for date in data[\"日期\"]:\n",
    "        price = get_price(\n",
    "            \"000001.XSHG\",\n",
    "            start_date=date,\n",
    "            end_date=date,\n",
    "            frequency=\"1d\",\n",
    "            fields=None,\n",
    "            adjust_type=\"pre\",\n",
    "            skip_suspended=False,\n",
    "            market=\"cn\",\n",
    "            expect_df=True,\n",
    "            time_slice=None,\n",
    "        )\n",
    "        if isinstance(price, pd.DataFrame):\n",
    "            price = price.close[-1]\n",
    "        else:\n",
    "            price = prices[-1]\n",
    "        prices.append(price)\n",
    "    data[\"上证指数\"] = prices\n",
    "    df = pd.DataFrame(data=data)\n",
    "    df.index = df[\"日期\"]\n",
    "    df.to_csv(file_name)\n",
    "\n",
    "    df[\"上证指数\"] = [i - 3000 for i in data[\"上证指数\"]]\n",
    "\n",
    "    fig = sns.lineplot(data=df)\n",
    "    fig.get_figure().set_figwidth(15)  # 设置宽度\n",
    "    fig.get_figure().set_figheight(6)  # 设置高度\n",
    "    plt.title(f\"a股自{dates[0]}至{today}本年度全A股涨幅中位数变化\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "stocks = getStocks()\n",
    "drawPic(stocks)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、当日涨跌幅分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "股票数：4842\n",
      "计算完成：\u001b[1;92m====================================================================================================>100%\u001b[0m\n",
      "中国股市自\u001b[1;92m2021-12-31\u001b[0m收盘至\u001b[1;92m2022-10-21\u001b[0m共获取到\u001b[1;31m2143\u001b[0m支股票的数据，开始分析...\n",
      "##################################################\n",
      "\u001b[1;92m2022-10-21\u001b[0m日共获取到\u001b[1;31m2143\u001b[0m支a股的数据，开始分析...\n",
      "##################################################\n",
      "2022-10-21日你需要收益率达到\u001b[1;31m0.80%\u001b[0m才能超越70.00%的股票!\n",
      "如果你是红的就超过了\u001b[1;31m45.78%\u001b[0m的股票！\n"
     ]
    },
    {
     "data": {
      "image/png": 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YSSYYDFJZWcnj5c2EFBamdVBRUUF1dTUawQNKCzMTOX1WFo9vqeOTF8yLfsDGTGGRJoNWVX00qpGYSaeyspKfPLSaTcFCAqFO7n3sGeaVLaFq8zpyi0qZPsSyoVCQ6upqzpmZzf+uO8SexqMU52WMW+zGTDWRdhO9ICKPicgXReTqviGqkZlJIS1/NvXtMDPQQ05BIQVz5jGtYOawyzXV13HPs9uoP9QEwL2rtkY7VGOmtEhbBofcMPTdQsb0U9OqKDAjsRNIHtGy2QWFzJs3n9y6naze1RqV+IwxnoiSgap+O9qBmMlpX6uSlZJIuq+XkSaDPnPShY2NXew51EZxbtrYBmiMASK/A/kf4VcRueGZaAdnJrbmziAH22FBQfoJj6oeqaIMb+EnttSNUWTGmP4i7Sa6JuxzCnARYGfzzJBe3NuGAgump+O6/kclLVE4LT+ZJzbXcfXy0jGLzxhzXEQtA1XdGjasU9WbgHdFOTYzwa3e3Up6IuSnj657KNwFczPYWtvCrkZ7kqkx0RBRy2CAK4fmA/YqKjOoI+3dbKxtp2yaIKfSR+S8pTiNX61t5InNtVxz4YIxiNAYEy7SS0vzw4Y8vJvP3hmtoMzE99dtBwnq8f7+UxEKBWmt38figgB/fGU3FRUV9hY0Y8bYiK4mEpEZbtyeD2AG1HfX8YMv1pIbgGlJEdxqPIym+jruqWwmkH8a25qU7zzwAt/6ICxcuHAMIjbGQORXEy0Tka3An4FHReQ1EXlTdEMzE1FlZSU/fHAN6/e3k9h6kJaW5jFZb3ZBIWeWlQDQkjLUvcvGmNGI9GqiW4GLVbUGQEQKgUeAc6MVmJm42tMKCbUqxVn+MV1veiCBwmkB9h7tHNP1GmMiP2eQ1JcInDoivINIRHJEZI2I3ODG54nIehGpFJHrwua73pW9IiIlEcZl4tC+ViUjkECmb+yvMSgryKC5G/YcsTegGTOWIk0GD4vIX0XkP0Xky8BTwP3DLSQiicBKYEdY8TeB7wELgXeLyGIRKQYud2XfAW4aQR1MHGntCnKg7dRvNBtMaYH3WOs19ngKY8bUcK+9/A8RyVTV7wLX411O2oV3QK8ebuWq2gNcBqwJK34r8KSqBoHH3fgKYKUrWwmcN4q6mDjw/N42QsCCgujck5iWnEBBCjy76ygayXOwjTERGa5lcI2qtgCo6suqequq/lRV1wL/FckGVLW+X1GaqvZ1+tYDM4ACoNHN3wv4ReSkDmcR+ZSIrBORdQ0NDZFs3oyzVdVHSU+E6ZmnfqPZYIoyhL3NPew4aK0DY8bKcMlgqDOAo/1r7/9zTgYpO3lB1dtVdamqLs3Pzx/l5k201B/tZGNdB8UZY3Oj2WBmpSoC3POPLXbPgTFjZLhksEFETnrxvYhcDuwb5TaPikjAfS7Aax3U4d3MhogkAOq6jMwE8sTmOkIKxWNwo9lQOg7XkUEbj21r4scPrqaysjKq2zNmKhguGXwa+IDrmvmViPxcRNYA3wD+dZTb/BtwqesGugx42g3vcGWXcOI5BjNBPLqplvk5SWQlRzcZAMzJ8HG0B/y5s6O+LWOmgiHvM1DVZuBiEVmEd6WPAL9Q1S2nsM0bgMeAm4E7VbUcQETuAiqANrwkYSaIYDDImle38+reI1w+V1CNfjKYntzL9lbY3WInkY0ZC5E+jqIcKB/tRlT17rDPDcCyAea5DbhttNswsVNZWcmNj24BMqnevpnCouIh3288FpJ9yry8dHYdbqU7aAnBmFMV6X0GxgypgUwKswJML8gbt20umZVJVxBe2muPtTbmVFkyMKes6nAXzd1QNmN833dUlJNKagKs3DE2zz8yZiqzZGBO2cqKFvwCC6ePbzLwiTA/S9hQ20Flvd1zYMypsGRgTkl7dy/PVB1lTroQSBzbB9NFojRLSPTBPS/sHvdtGzOZWDIwp+TxzXW094SYnxX9K4gGEkgQVszP4KH1NTS328v3jBktSwZm1FSV+17aw5ysRPJTYhfHexdPo6MnyH0v74ldEMZMcJYMzKit3XWYzTXNvHfxtKg+fmI483OSWb4wnzvWVNPW1RuzOIyZyCwZmFH79ZpdZKcm8rbS8T1xPJDPX7SApvYe7n3RWgfGjIYlAzMqlfWtPFN+kH9dVkwgIfb/jc4syuatZfncvrrKzh0YMwqx/ys2E9ItT+8gJdHPlW8qiWkcoVCQ6upqKioq+OCiAM0dPdzyzI7hFzTGnCDSdyAbc8y22hYe31zHNStKyUtP5lAMY2mqr+Oeymbm1gmH6vbxzrJF3PviHj70xiIWjvNNcMZMZNYyMCOiqvzoqXIyAwl88oJ5sQ4HgOyCQgrmzCN35hw+elYuGYEEvvLQJnqCoViHZsyEYcnAjMjfth1kVUUDn71wAVkpibEO5yRZAT/fe+/pbKpp5mf/sPccGBMpSwYmYq0d3Vz/yCaKpyVxbk4H27Zto6Kigurq6rh4H3Hf+YPS5BYump/BT5/ZyZqd9npUYyJh5wxMxL7+wFoOtvZy0WwfN97xML5AKnPLllC1eR25RaVRf2z1cMLPHwTr9jIjdRaf+c06bnnXbFacvRi/f/wfl2HMRGEtAxORv5cf5LHyZhZOE163cD7TCmYe66ufVjAz1uEd0xdTUoKPgrYqeoIhrnmkmuc3bo91aMbENUsGZlj7Drdz7R82MTc7iTPyYnen8UjNLMjnfWfPISgJfGXlfnY12nsPjBmMJQMzpJb2Lq789fP09Aa5qrQH38TJBQAUZARYMdtHR0+Iy3/+PGt3HY51SMbEJUsGZlBdvUGuuuN5djV1sTQfnliznubmI7EOa8Syk0Jce4aSngj/8usX+eP6fbEOyZi4Y8nADKgnGOKLD2xkQ20H5073ccai+XF1bmAkmurreHLtds7NC5IuXVz74Gaue2QLHd3BWIdmTNywq4nMSdo6u/nYHc+ztqad988XEiZa39AAsgsKmV0yn4vYSXMX/Pblvawpr+NnH3kjp8/JjnV4xsSctQzMCVq7evnwL59jbU0bSwuEvVsnZtfQYFoa6jhaU86KWT4OtnRy+S9e4Oanymnvtkdfm6nNkoE55kBzJ//v1y/x2sEOzpvh482nl07YrqGhZBcU8vpF83lniZ+z8+Fn/6hi+U3P8OTm2ri4ec6YWLBuIgPAC5UNXH3ferp6Q3x6sdDUPfG7hobTfqgOaWnmotmLebm2g6t/9ypnFe3icxeWMoOmYy/sKS0ttRvWzKRnyWCKC4WUO5/fxfef3E5AelgxJ5n16+PjjuLxkF1QSNnC+eQk7yTBJzxd28pVd68jy9fF66ankHK0hi9fAQsXLox1qMZElSWDKWx3Yxtff3gLL1Yf4ryiNGYldjCrZD69TftjHdq4a26oo6WlmRWli1m7fS8Hk2fzfF2IgL+Q3PWHuDKjmcUzM2P6ek9josmSwRRU39LJL5+t5jcv7SbRB194cz5l/kZW103tA112QSEzi+dTVr+fRcmtJBUuYn1lLQ9uaeKBzc8xIzPAikX5nFWUzeLCTBYUZJCU4CMYDFJZefwJqdatZCYiSwZTRGdPkFUVDTyxpY6nXjtAbyjE20sz6ajdwcEGHy9sXj9luoYiIQJz89JIaYMz84V6fz4v723lT6/WcP9a76a1RL8wJzuV7KQQBxsayc1KJ9R6iE9c1Mmb37CIvPSkCdmSsOQ2NVkymKRaO7r56yvb2FbfyWsHO9hU10FHr5KZ7OPi0nTe97psOhv38Szeg90OHaiJdchxqam+jj9VNjO3zEfPjld4fSCVvJLF7KlrYFZBLu1+Ye+hDhpCadQ0KpDDF56ogSdqSPILs7JTmDUtlZlZyQSCHRSkJ5Cb4qMgLYHpGUkkJfji7mBbWVnJTx5aTe7MORyq28e177dzJlNBXCUDEbke+CjQBFyhqrtjG1H8CP+11tvbS3uPcqRLOdTeiz8jl4bWbg40d1C+r5E9R7qpb+2l7yLJ5FAnWb5Ols3KobVqHQ0VqfxV4+fR0/Gu70mohw7U4A+kUTp/Pj2H99O8r5y5ZUvoatjA4qJSSk47k03rXuJQWyfp+UU0NLWQLUpjc5Atextp7j553UkSZGZ6JXNy0ihIT+TM0kLKpmeyYHoGOWlJ419ZJ3fmHArmxMeb7Mz4iJtkICLFwOXAQuAS4CbggzENKoa6e0PUNLWz53A7ew+1s6mqhucqDtDtS6GlK0hIwn9J1gIQ8EOC9pCTlkRhTy3T86Zx9uuXsGfTC/gDaZQtms/2o/vxB9KsNTAGwpMEQFKCj3R/L1kFWZSdXsr2V1bT0rCfhWVLyNy3gWlzSilc8Hpe27ieLn8qafmz2LevhiNHQxzuhLYe5eGtR46tPzPZR9G0JIqmJbGkeAYzslKYnhlgRlYy+ekB0pL9JPjH7lah3mCIls5e9rd0c6hT6TjUxuFWZVt9B4G8NvIzkklNiptDhhlj8bRnVwArVTUoIiuBn0VjI6/saqRq9z4EQGD2rFn4/X40FKK2dj8+AQ2F8ImQmOBHBEqKi0j0+/H5BJ8IfhFEvH7l3qDS2dNL9e49BENKV0+QYEgJiY9gSMkrmEEIoaunl/21B+hVpac3RE9ICanQ2ROktTtEe6/S2hUi6E+ipqmTuuYOQmH3PyX5ICUhhZzMNDJb6klN8lNSXELjrq30th9hQekC9rzm/dIvO30x21+pxZ/ca3+8MXZCq0IgKyWRnIRu/IFEyubmktm4FX92GmWnL2Db2tU0Hm0jc1YZ1bv30kU6B5qnUXGwjScrWgZcf7JfSE3ykZLgIyVRCCQIiX4ffoGM9HQS/D78Puhoa8Pv8y4l7gkpPUFo7wnS1h2itVtp6w7S0dvvhru93o+M1bX74QnvCrPs1ETX9ZVCGl3kpPjJSPazoLiQ9EASPlEO1NaS4BOEEH7B/R0JxUVF+P1+fAIiggChUIh9e/cA7jxNSQkJCX5CwRB79uxG+srnziXB78fX97eHID7cdG9dfdNCwSBV1dXHlhVgQWkpCS6OeKKqqHKsFa+qKLgyrzTYG6SyqsobU5g3fx7pgbE/HxVPR4oCoBFAVXtFxC8iflUd06eJXfv7Dext7gkrqY1gqVN5ymXdsHP4Qr0k+sEX7KZwWipleeksL8kmoeMIL23cSn5OJvU7N5Mzex5zc5ZQVVOBL5BKdk8ahw/vIRBIPfb8oKb6Wur3ZXOkvg5fIHXSfQbiIo5ofG5uqCM5kEphmtARrMeX2MrcolyqNm/gSMtRcosWULN3L8HkDNLyCjnc2IAmBAhkZRPs9nGgtZWQP4mkQAq9Pb3kZnWQmJRER1c3h492IP4Eeru78YmSnJRIT3srfkJkpKUQONJAmig52dNoObiXrJxc5swpprZyGy0dXWQWzKa5tZ15M+fQQSKbdh2kvi2shfp8fQR/C3sjmGfPIOW7I1h2KFUnjPUlivBxwsokbET6zYOcON53kNYTxo8f1I+VwQkH/tGrpvy77yCQOLbnmeIpGfT/jk5KeyLyKeBTbrRVRCpGua08XOKJN+UjXyRu6zJCk6UeMInr8kIMAxkDk2W/5KXcdEr1KB6oMJ6SQR1wGoCIJADav1WgqrcDt5/qhkRknaouPdX1xIPJUpfJUg+wusSryVKXaNUjnh5U9zTwDhHx451AXhPjeIwxZsqIm5aBqh4QkbuACqANuCzGIRljzJQRN8kAQFVvA24bh02dcldTHJksdZks9QCrS7yaLHWJSj3Ent9ujDEmns4ZGGOMiZFJmQxE5FIRqXDDOweZ56siUt2v7JciUiUi/xCRuHgx7nB1EZE3ishWEdkpIh9zZQtEpElENrrh3PGP/KQ4rxeRShF5RURK+k37hIv/NRE525XliMgqtz9+HpOgBzGKunxSRPaF7Y+4eRDRMHUJiMgfReTusLK43C+jqMeE2ycikiUi94tIuZu2xJWPzT7x7oCbXAOwFZgFzAR2AL5+078J3ALsDiu7AHjUff4scFOs6xFhXf4OnAWk4Z18zwaWAXfGOvawGIuBDYAf78KAB8KmpQA7gXTgdOAlV/4t4Evu8x+Bd8a6HqdQl68BV8Y69pHUxU3vexLA3WFlcbdfRlmPCbdP3P+vS9zny4AHx3KfTLqWgcukR1R1v6rW4d0+3P+Ri7er6hf6lV0IPOY+/wlYHr0oIzNcXdz9GPNVdYOqtgHP4SWCHOLr5ppjjxrB+8M8L2zaucBaVW1V1S1AnoikEof7wxlNXeJtf/QZqi7gPTTywX5l8bhfRlOPCbdPVLVDVZ90o7vxfvjBGO2TSZcMCHushVMPzAifQVUHunc+fLmTlomR4eqSAxwZYHoi8CHXVfGgiORFO9BhnPCoEcAf1izvX8cGYDrxuT9gdHVJBH7kuvNulvh5QM5QdZmQfycjqMeE3CdhrgCe6r8Mp7BPJmMyGOjyqEh29LCPw4iB4eoy4HRV/TNQrKqvA7YA34lGcCMw1Hc7WB3jcX/A6OryJbcvlgJL8P6Q48FovuN43C+jiWnC7hMRORN4L8cvwx+TfTJpkoGIXCciG4Ef4D2DpE8BXrYcTl3YcpEuExUjqMshIHOg6eo6EPGax4ujFmxkjn23AzxqJPx7B8jH+0UdN/ujnxHXpW9fqGoHXnM+1vujz1B1GXYZ4me/jLgeE3WfuFb+3cCHXewnLMMp7JNJkwxU9Xuq+gZVfRsQEJFZIjITmANsFZEPiMh1Q6ziKeDd7vPleI/HiIlI66KqIWCbiJwtIml4J8HXiMiMsGbv24CNManIcSc9asQ1zd8OrAXOFJF0ETkD7+B5lDjaH/2MuC5u3yEiPrz+3Y0xir2/oeoymHjcLyOux0TcJy45/B74uqpuDVtmTPZJXN2BPIY+i3eVjR+4RlVVRPKB2YMtoKqvuD72KrznWsfL4zCGq8sXgIeBVOBGVW0WkfcAXxORLrznBn9i/MM+Tgd+1MiPgRxV7RaRbwKvAiHgQ26x24CHReRq4G+q+pcYhH6SUdblOhFZDgTx/lD/PN5xD2SougyxWNztl1HWYyLukyvwTih/X0S+7xZZzhjtE7sD2RhjzOTpJjLGGDN6lgyMMcZYMjDGGGPJwBhjDJYMjDHGYMnAGGMMlgyMOWUiMl1Ebo51HMacCrvPwBhjjLUMjBkNESkRkUdF5GH3wpF1rvzfRGSziGwXkatc2adFZL2IrBORc2IauDGDmKyPozBmPFwMnAF0Ag+5si/gPeumAUgVkULgfcAb8Z6O+XPg/HGP1JhhWMvAmNHbqarl/cruwXtW1AXuhUPnAGfivb3qN0Du+IZoTGSsZWDM6HX2L1DVm0Xkr8BtInIfcBB4RFU/Pe7RGTMC1jIwZgyJSK6qbgJuBN4EvAKcLyLpfdNjGZ8xg7GWgTFj6xERyQJ6gc+o6n73uOHn3Dsm/gJ8NaYRGjMAu7TUGGOMdRMZY4yxZGCMMQZLBsYYY7BkYIwxBksGxhhjsGRgjDEGSwbGGGOwZGCMMQZLBsYYY4D/A5v0xMpO2kYOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x129.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "下跌\u001b[1;92m981\u001b[0m家，占比：45.78%\n",
      "上涨\u001b[1;31m1039\u001b[0m家，占比：48.48%\n",
      "平盘\u001b[1;94m123\u001b[0m家，占比：5.74%\n",
      "中位数：\u001b[1;31m0.0%\u001b[0m\n",
      "平均数：\u001b[1;31m0.3%\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2143.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.002976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.023868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.008762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.010753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              rise\n",
       "count  2143.000000\n",
       "mean      0.002976\n",
       "std       0.023868\n",
       "min      -0.100000\n",
       "25%      -0.008762\n",
       "50%       0.000000\n",
       "75%       0.010753\n",
       "max       0.200000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### import datetime\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import traceback\n",
    "from text import *\n",
    "import datetime\n",
    "\n",
    "\n",
    "def save2csv(df, name):\n",
    "    basePath = os.path.abspath(os.path.dirname('__file__'))\n",
    "    isExists = os.path.exists(os.path.join(basePath, 'csv'))  # 判断csv文件夹是否存在\n",
    "    if not isExists:\n",
    "        os.makedirs(os.path.join(basePath, 'csv'))  # 如果不存在就创建\n",
    "    csvPath = os.path.join(os.path.join(basePath, 'csv'), name)\n",
    "    # print(csvPath)\n",
    "    df.to_csv(csvPath, encoding='utf_8_sig')\n",
    "###################################################################################\n",
    "\n",
    "today=datetime.date.today()\n",
    "if today.weekday()>4:\n",
    "    n=2\n",
    "else:\n",
    "    n=1\n",
    "start=f'{today.year-1}-12-31'\n",
    "today=f'{today.year}-{today.month}-{today.day}'\n",
    "yesterday=get_previous_trading_date(today,n,market='cn')\n",
    "\n",
    "\n",
    "\n",
    "def getStocks():\n",
    "    \"获取股票数据\"\n",
    "    df = all_instruments(type=\"CS\", market=\"cn\", date=today)\n",
    "    index = df.order_book_id\n",
    "    df = get_price(\n",
    "        index,\n",
    "        start_date=yesterday,\n",
    "        end_date=today,\n",
    "        frequency=\"1d\",\n",
    "        adjust_type=\"pre\",\n",
    "        skip_suspended=False,\n",
    "        market=\"cn\",\n",
    "        fields=\"close\",\n",
    "        expect_df=False,\n",
    "    )\n",
    "    print(f\"股票数：{len(index)}\")\n",
    "    stocks = []\n",
    "    maxS = [0, 0]\n",
    "    minS = [0, 0]\n",
    "    for code in df.columns:\n",
    "        a = df[code][0]\n",
    "        b = df[code][-1]\n",
    "        if a > 0 and b > 0:\n",
    "            rise = b / a - 1\n",
    "            stocks.append([code, rise])\n",
    "            # name=get\n",
    "            if rise > maxS[-1]:\n",
    "                maxS = [code,  rise]\n",
    "            if rise < minS[-1]:\n",
    "                minS = [code,  rise]\n",
    "\n",
    "    print(f\"计算完成：{OKGREEN}\" + \"=\" * 100 + f\">100%{ENDL}\")\n",
    "    print(\n",
    "        f\"中国股市自{OKGREEN}{start}{ENDL}收盘至{OKGREEN}{today}{ENDL}共获取到{OKRED}{len(stocks)}{ENDL}支股票的数据，开始分析...\"\n",
    "    )\n",
    "    print(\"#\" * 50)\n",
    "    #print(f\"涨幅最高的是：{OKRED}{instruments(maxS[0]).symbol}{ENDC}\")\n",
    "    #print(f\"涨幅最低的是：{OKGREEN}{instruments(minS[0]).symbol}{ENDC}\")\n",
    "    return stocks\n",
    "def drawPic(stocks):\n",
    "    '绘制图表'\n",
    "    print(f'{OKGREEN}{today}{ENDL}日共获取到{OKRED}{len(stocks)}{ENDL}支a股的数据，开始分析...')\n",
    "    print('#'*50)\n",
    "    rises=[i[1] for i in stocks]\n",
    "    ratio=0.7\n",
    "    need=len(stocks)*ratio\n",
    "    need=int(need)\n",
    "    newRises=sorted(rises)\n",
    "    print(f'{today}日你需要收益率达到{OKRED}{newRises[need]*100:.2f}%{ENDC}才能超越{ratio*100:.2f}%的股票!')\n",
    "    c=0\n",
    "    d=0\n",
    "    for rise in rises:\n",
    "        if rise<0:\n",
    "            c+=1\n",
    "        if rise>0:\n",
    "            d+=1\n",
    "    print(f'如果你是红的就超过了{OKRED}{(c/len(rises))*100:.2f}%{ENDC}的股票！')\n",
    "    data={\n",
    "        'index':[i[0] for i in stocks],\n",
    "        'rise':[i[1] for i in stocks],\n",
    "    }    \n",
    "    df=pd.DataFrame(data=data)    \n",
    "    plt.title(f'a股{today}日涨跌幅度分布')    \n",
    "    #plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    #sns.set_theme(style=\"whitegrid\",font='NotosansCJK',font_scale=1.4)\n",
    "    #sns.set(rc={'figure.figsize':(8,2)})#设置尺寸    \n",
    "    fig=sns.histplot(df['rise'],bins=100,kde=True)    \n",
    "    #plt.gcf().set_size_inches(8, 2)#同时设置宽度和高度\n",
    "    fig.get_figure().set_figwidth(6)#设置宽度\n",
    "    fig.get_figure().set_figheight(1.8)#设置高度\n",
    "    plt.show()  \n",
    "    print(f'下跌{OKGREEN}{c}{ENDC}家，占比：{c/len(rises)*100:.2f}%')\n",
    "    print(f'上涨{OKRED}{d}{ENDC}家，占比：{(d/len(rises))*100:.2f}%')    \n",
    "    print(f'平盘{OKBLUE}{len(rises)-c-d}{ENDC}家，占比：{100-((c+d)/len(rises))*100:.2f}%')  \n",
    "    mid=round(newRises[len(newRises)//2]*100,2)\n",
    "    ave=round(sum(newRises)/len(newRises)*100,2)\n",
    "    print(f'中位数：{OKRED}{mid}%{ENDL}')\n",
    "    print(f'平均数：{OKRED}{ave}%{ENDL}')\n",
    "    return df[['rise']].describe()\n",
    "stocks=getStocks()\n",
    "drawPic(stocks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、近一年成交额最多的股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "股票数：5068\n",
      "\n",
      "中国股市自2021-10-21至2022-10-20共获取到\u001b[1;31m4455\u001b[0m支股票的数据，开始分析...\n",
      "##################################################\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m最高的\u001b[0m是：\u001b[1;31m宁德时代\u001b[0m\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m最低的\u001b[0m是：\u001b[1;92m秦川物联\u001b[0m\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m平均值\u001b[0m是：\u001b[1;31m517.06\u001b[0m亿\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m中位数\u001b[0m是：\u001b[1;92m266.11\u001b[0m亿\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m前10(0.22%)\u001b[0m占总成交额：\u001b[1;31m5.17%\u001b[0m\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m前50(1.12%)\u001b[0m占总成交额：\u001b[1;31m14.28%\u001b[0m\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m前100(2.24%)\u001b[0m占总成交额：\u001b[1;31m21.64%\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x129.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国股市自2021-10-21至2022-10-20累积成交额\u001b[1;31mTOP50\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票代码</th>\n",
       "      <th>股票名称</th>\n",
       "      <th>累积成交额(亿)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300750.XSHE</td>\n",
       "      <td>宁德时代</td>\n",
       "      <td>18539.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600519.XSHG</td>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>14388.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>002466.XSHE</td>\n",
       "      <td>天齐锂业</td>\n",
       "      <td>13618.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>002594.XSHE</td>\n",
       "      <td>比亚迪</td>\n",
       "      <td>13172.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>601012.XSHG</td>\n",
       "      <td>隆基绿能</td>\n",
       "      <td>12176.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>300059.XSHE</td>\n",
       "      <td>东方财富</td>\n",
       "      <td>11887.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000858.XSHE</td>\n",
       "      <td>五粮液</td>\n",
       "      <td>10087.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>600111.XSHG</td>\n",
       "      <td>北方稀土</td>\n",
       "      <td>8824.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>002460.XSHE</td>\n",
       "      <td>赣锋锂业</td>\n",
       "      <td>8557.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>300274.XSHE</td>\n",
       "      <td>阳光电源</td>\n",
       "      <td>7873.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>600438.XSHG</td>\n",
       "      <td>通威股份</td>\n",
       "      <td>7507.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>300014.XSHE</td>\n",
       "      <td>亿纬锂能</td>\n",
       "      <td>7103.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>600036.XSHG</td>\n",
       "      <td>招商银行</td>\n",
       "      <td>6977.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>000625.XSHE</td>\n",
       "      <td>长安汽车</td>\n",
       "      <td>6973.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>603259.XSHG</td>\n",
       "      <td>药明康德</td>\n",
       "      <td>6853.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>002432.XSHE</td>\n",
       "      <td>九安医疗</td>\n",
       "      <td>6673.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>601318.XSHG</td>\n",
       "      <td>中国平安</td>\n",
       "      <td>6414.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>000792.XSHE</td>\n",
       "      <td>盐湖股份</td>\n",
       "      <td>6258.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>002475.XSHE</td>\n",
       "      <td>立讯精密</td>\n",
       "      <td>5976.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>603799.XSHG</td>\n",
       "      <td>华友钴业</td>\n",
       "      <td>5834.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>600096.XSHG</td>\n",
       "      <td>云天化</td>\n",
       "      <td>5783.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>002241.XSHE</td>\n",
       "      <td>歌尔股份</td>\n",
       "      <td>5761.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>601919.XSHG</td>\n",
       "      <td>中远海控</td>\n",
       "      <td>5705.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>601888.XSHG</td>\n",
       "      <td>中国中免</td>\n",
       "      <td>5617.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>002176.XSHE</td>\n",
       "      <td>江特电机</td>\n",
       "      <td>5477.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>601899.XSHG</td>\n",
       "      <td>紫金矿业</td>\n",
       "      <td>5440.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>002129.XSHE</td>\n",
       "      <td>TCL中环</td>\n",
       "      <td>5330.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>002371.XSHE</td>\n",
       "      <td>北方华创</td>\n",
       "      <td>5209.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>600089.XSHG</td>\n",
       "      <td>特变电工</td>\n",
       "      <td>5115.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>002709.XSHE</td>\n",
       "      <td>天赐材料</td>\n",
       "      <td>5105.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>002714.XSHE</td>\n",
       "      <td>牧原股份</td>\n",
       "      <td>4959.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>000002.XSHE</td>\n",
       "      <td>万科A</td>\n",
       "      <td>4837.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>600031.XSHG</td>\n",
       "      <td>三一重工</td>\n",
       "      <td>4708.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>600905.XSHG</td>\n",
       "      <td>三峡能源</td>\n",
       "      <td>4699.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>000723.XSHE</td>\n",
       "      <td>美锦能源</td>\n",
       "      <td>4659.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>000568.XSHE</td>\n",
       "      <td>泸州老窖</td>\n",
       "      <td>4584.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>600276.XSHG</td>\n",
       "      <td>恒瑞医药</td>\n",
       "      <td>4512.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>000333.XSHE</td>\n",
       "      <td>美的集团</td>\n",
       "      <td>4479.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>600030.XSHG</td>\n",
       "      <td>中信证券</td>\n",
       "      <td>4455.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>000651.XSHE</td>\n",
       "      <td>格力电器</td>\n",
       "      <td>4445.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>600703.XSHG</td>\n",
       "      <td>三安光电</td>\n",
       "      <td>4389.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>601166.XSHG</td>\n",
       "      <td>兴业银行</td>\n",
       "      <td>4375.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>600887.XSHG</td>\n",
       "      <td>伊利股份</td>\n",
       "      <td>4360.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>002240.XSHE</td>\n",
       "      <td>盛新锂能</td>\n",
       "      <td>4255.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>603501.XSHG</td>\n",
       "      <td>韦尔股份</td>\n",
       "      <td>4243.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>603986.XSHG</td>\n",
       "      <td>兆易创新</td>\n",
       "      <td>4209.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>600522.XSHG</td>\n",
       "      <td>中天科技</td>\n",
       "      <td>4167.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>600418.XSHG</td>\n",
       "      <td>江淮汽车</td>\n",
       "      <td>4129.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>002415.XSHE</td>\n",
       "      <td>海康威视</td>\n",
       "      <td>4081.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>600196.XSHG</td>\n",
       "      <td>复星医药</td>\n",
       "      <td>4075.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           股票代码   股票名称  累积成交额(亿)\n",
       "1   300750.XSHE   宁德时代  18539.27\n",
       "2   600519.XSHG   贵州茅台  14388.08\n",
       "3   002466.XSHE   天齐锂业  13618.49\n",
       "4   002594.XSHE    比亚迪  13172.44\n",
       "5   601012.XSHG   隆基绿能  12176.38\n",
       "6   300059.XSHE   东方财富  11887.92\n",
       "7   000858.XSHE    五粮液  10087.47\n",
       "8   600111.XSHG   北方稀土   8824.56\n",
       "9   002460.XSHE   赣锋锂业   8557.52\n",
       "10  300274.XSHE   阳光电源   7873.02\n",
       "11  600438.XSHG   通威股份   7507.73\n",
       "12  300014.XSHE   亿纬锂能   7103.98\n",
       "13  600036.XSHG   招商银行   6977.32\n",
       "14  000625.XSHE   长安汽车   6973.49\n",
       "15  603259.XSHG   药明康德   6853.32\n",
       "16  002432.XSHE   九安医疗   6673.73\n",
       "17  601318.XSHG   中国平安   6414.17\n",
       "18  000792.XSHE   盐湖股份   6258.24\n",
       "19  002475.XSHE   立讯精密   5976.66\n",
       "20  603799.XSHG   华友钴业   5834.01\n",
       "21  600096.XSHG    云天化   5783.11\n",
       "22  002241.XSHE   歌尔股份   5761.05\n",
       "23  601919.XSHG   中远海控   5705.10\n",
       "24  601888.XSHG   中国中免   5617.41\n",
       "25  002176.XSHE   江特电机   5477.69\n",
       "26  601899.XSHG   紫金矿业   5440.07\n",
       "27  002129.XSHE  TCL中环   5330.64\n",
       "28  002371.XSHE   北方华创   5209.32\n",
       "29  600089.XSHG   特变电工   5115.10\n",
       "30  002709.XSHE   天赐材料   5105.54\n",
       "31  002714.XSHE   牧原股份   4959.71\n",
       "32  000002.XSHE    万科A   4837.69\n",
       "33  600031.XSHG   三一重工   4708.41\n",
       "34  600905.XSHG   三峡能源   4699.99\n",
       "35  000723.XSHE   美锦能源   4659.88\n",
       "36  000568.XSHE   泸州老窖   4584.32\n",
       "37  600276.XSHG   恒瑞医药   4512.20\n",
       "38  000333.XSHE   美的集团   4479.43\n",
       "39  600030.XSHG   中信证券   4455.76\n",
       "40  000651.XSHE   格力电器   4445.84\n",
       "41  600703.XSHG   三安光电   4389.59\n",
       "42  601166.XSHG   兴业银行   4375.07\n",
       "43  600887.XSHG   伊利股份   4360.92\n",
       "44  002240.XSHE   盛新锂能   4255.49\n",
       "45  603501.XSHG   韦尔股份   4243.87\n",
       "46  603986.XSHG   兆易创新   4209.96\n",
       "47  600522.XSHG   中天科技   4167.99\n",
       "48  600418.XSHG   江淮汽车   4129.37\n",
       "49  002415.XSHE   海康威视   4081.15\n",
       "50  600196.XSHG   复星医药   4075.83"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "from text import *\n",
    "\n",
    "\n",
    "today = datetime.date.today()\n",
    "if today.weekday() > 4:\n",
    "    n = 2\n",
    "else:\n",
    "    n = 1\n",
    "start = f\"{today.year-1}-{today.month}-{today.day}\"\n",
    "# start=f'{today.year}-1-1'\n",
    "today = f\"{today.year}-{today.month}-{today.day}\"\n",
    "yesterday = get_previous_trading_date(today, n, market=\"cn\")\n",
    "if datetime.datetime.now().hour < 16:\n",
    "    today = yesterday\n",
    "\n",
    "\n",
    "def getStocks():\n",
    "    \"获取股票数据\"\n",
    "    df = all_instruments(type=\"CS\", market=\"cn\", date=None)\n",
    "    index = df.order_book_id\n",
    "    print(f\"股票数：{len(index)}\")\n",
    "    codes = []\n",
    "    names = []\n",
    "    moneys = []\n",
    "    maxS = [0, 0]\n",
    "    minS = [0, 0]\n",
    "    df = get_price(\n",
    "        index,\n",
    "        start_date=start,\n",
    "        end_date=today,\n",
    "        frequency=\"1d\",\n",
    "        adjust_type=\"pre\",\n",
    "        skip_suspended=False,\n",
    "        market=\"cn\",\n",
    "        fields=\"total_turnover\",\n",
    "        expect_df=False,\n",
    "    )\n",
    "    for code in df.columns:\n",
    "        if df[code][0] > 0 and df[code][-1] > 0:\n",
    "            money = sum(df[code])\n",
    "            name = instruments(code).symbol\n",
    "            moneys.append(round(money / 100000000, 2))\n",
    "            names.append(name)\n",
    "            codes.append(code)\n",
    "    max_money = max(moneys)\n",
    "    index = moneys.index(max_money)\n",
    "    max_name = names[index]\n",
    "    min_money = min(moneys)\n",
    "    index = moneys.index(min_money)\n",
    "    min_name = names[index]\n",
    "    ave = round(sum(moneys) / (len(moneys)), 2)\n",
    "    mid = sorted(moneys)[len(moneys) // 2]\n",
    "    top10 = sum(sorted(moneys)[-1:-11:-1])\n",
    "    top50 = sum(sorted(moneys)[-1:-51:-1])\n",
    "    top100 = sum(sorted(moneys)[-1:-101:-1])\n",
    "\n",
    "    print()\n",
    "    print(f\"中国股市自{start}至{today}共获取到{OKRED}{len(moneys)}{ENDL}支股票的数据，开始分析...\")\n",
    "    print(\"#\" * 50)\n",
    "    print(f\"{OKBLUE}累积成交额{ENDL}{OKRED}最高的{ENDL}是：{OKRED}{max_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}累积成交额{ENDL}{OKRED}最低的{ENDL}是：{OKGREEN}{min_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}累积成交额{ENDL}{OKRED}平均值{ENDL}是：{OKRED}{ave}{ENDC}亿\")\n",
    "    print(f\"{OKBLUE}累积成交额{ENDL}{OKRED}中位数{ENDL}是：{OKGREEN}{mid}{ENDC}亿\")\n",
    "    print(\n",
    "        f\"{OKBLUE}累积成交额{ENDL}{OKRED}前10({10/len(moneys)*100:.2f}%){ENDL}占总成交额：{OKRED}{top10/sum(moneys)*100:.2f}%{ENDC}\"\n",
    "    )\n",
    "    print(\n",
    "        f\"{OKBLUE}累积成交额{ENDL}{OKRED}前50({50/len(moneys)*100:.2f}%){ENDL}占总成交额：{OKRED}{top50/sum(moneys)*100:.2f}%{ENDC}\"\n",
    "    )\n",
    "    print(\n",
    "        f\"{OKBLUE}累积成交额{ENDL}{OKRED}前100({100/len(moneys)*100:.2f}%){ENDL}占总成交额：{OKRED}{top100/sum(moneys)*100:.2f}%{ENDC}\"\n",
    "    )\n",
    "    df = pd.DataFrame(\n",
    "        {\n",
    "            \"股票代码\": codes,\n",
    "            \"股票名称\": names,\n",
    "            \"累积成交额(亿)\": moneys,\n",
    "        }\n",
    "    )\n",
    "    return df\n",
    "\n",
    "\n",
    "def drawPic(df, n=50):\n",
    "    \"绘制图表\"\n",
    "    fig = sns.histplot(df[\"累积成交额(亿)\"], bins=500)\n",
    "    # plt.gcf().set_size_inches(8, 2)#同时设置宽度和高度\n",
    "    fig.get_figure().set_figwidth(6)  # 设置宽度\n",
    "    fig.get_figure().set_figheight(1.8)  # 设置高度\n",
    "    plt.title(f\"a股自{start}至{today}成交额分布\")\n",
    "    plt.show()\n",
    "    # display(df[['成交额(亿)']].describe())\n",
    "    df = df.sort_values(by=\"累积成交额(亿)\")\n",
    "    df = df[-1 : -n - 1 : -1]\n",
    "    df.index = [i for i in range(1, len(df) + 1)]\n",
    "    print(f\"中国股市自{start}至{today}累积成交额{OKRED}TOP{n}{ENDL}\")\n",
    "    display(df)\n",
    "\n",
    "\n",
    "df = getStocks()\n",
    "drawPic(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、近三月成交额最多的股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "股票数：5068\n",
      "\n",
      "中国股市自2022-7-21至2022-10-20共获取到\u001b[1;31m4719\u001b[0m支股票的数据，开始分析...\n",
      "##################################################\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m最高的\u001b[0m是：\u001b[1;31m宁德时代\u001b[0m\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m最低的\u001b[0m是：\u001b[1;92m*ST恒誉\u001b[0m\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m平均值\u001b[0m是：\u001b[1;31m107.07\u001b[0m亿\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m中位数\u001b[0m是：\u001b[1;92m54.12\u001b[0m亿\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m前10(0.21%)\u001b[0m占总成交额：\u001b[1;31m5.02%\u001b[0m\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m前50(1.06%)\u001b[0m占总成交额：\u001b[1;31m13.44%\u001b[0m\n",
      "\u001b[1;94m累积成交额\u001b[0m\u001b[1;31m前100(2.12%)\u001b[0m占总成交额：\u001b[1;31m20.56%\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x129.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国股市自2022-7-21至2022-10-20累积成交额(亿)\u001b[1;31mTOP50\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票代码</th>\n",
       "      <th>股票名称</th>\n",
       "      <th>累积成交额(亿)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300750.XSHE</td>\n",
       "      <td>宁德时代</td>\n",
       "      <td>3933.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>002466.XSHE</td>\n",
       "      <td>天齐锂业</td>\n",
       "      <td>3180.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>600519.XSHG</td>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>2894.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>002594.XSHE</td>\n",
       "      <td>比亚迪</td>\n",
       "      <td>2543.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>600438.XSHG</td>\n",
       "      <td>通威股份</td>\n",
       "      <td>2415.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>601012.XSHG</td>\n",
       "      <td>隆基绿能</td>\n",
       "      <td>2349.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>300274.XSHE</td>\n",
       "      <td>阳光电源</td>\n",
       "      <td>2200.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>000625.XSHE</td>\n",
       "      <td>长安汽车</td>\n",
       "      <td>2089.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>300059.XSHE</td>\n",
       "      <td>东方财富</td>\n",
       "      <td>2003.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>000858.XSHE</td>\n",
       "      <td>五粮液</td>\n",
       "      <td>1739.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>300014.XSHE</td>\n",
       "      <td>亿纬锂能</td>\n",
       "      <td>1524.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>002460.XSHE</td>\n",
       "      <td>赣锋锂业</td>\n",
       "      <td>1476.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>002371.XSHE</td>\n",
       "      <td>北方华创</td>\n",
       "      <td>1411.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>603259.XSHG</td>\n",
       "      <td>药明康德</td>\n",
       "      <td>1398.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>002475.XSHE</td>\n",
       "      <td>立讯精密</td>\n",
       "      <td>1375.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>600418.XSHG</td>\n",
       "      <td>江淮汽车</td>\n",
       "      <td>1347.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>600036.XSHG</td>\n",
       "      <td>招商银行</td>\n",
       "      <td>1332.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>002129.XSHE</td>\n",
       "      <td>TCL中环</td>\n",
       "      <td>1321.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>002241.XSHE</td>\n",
       "      <td>歌尔股份</td>\n",
       "      <td>1240.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>300068.XSHE</td>\n",
       "      <td>南都电源</td>\n",
       "      <td>1200.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>000629.XSHE</td>\n",
       "      <td>钒钛股份</td>\n",
       "      <td>1200.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>002738.XSHE</td>\n",
       "      <td>中矿资源</td>\n",
       "      <td>1184.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>601888.XSHG</td>\n",
       "      <td>中国中免</td>\n",
       "      <td>1160.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>000821.XSHE</td>\n",
       "      <td>京山轻机</td>\n",
       "      <td>1143.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>600089.XSHG</td>\n",
       "      <td>特变电工</td>\n",
       "      <td>1123.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>603799.XSHG</td>\n",
       "      <td>华友钴业</td>\n",
       "      <td>1084.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>601318.XSHG</td>\n",
       "      <td>中国平安</td>\n",
       "      <td>1073.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>600256.XSHG</td>\n",
       "      <td>广汇能源</td>\n",
       "      <td>1030.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>002709.XSHE</td>\n",
       "      <td>天赐材料</td>\n",
       "      <td>1018.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>600703.XSHG</td>\n",
       "      <td>三安光电</td>\n",
       "      <td>995.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>601899.XSHG</td>\n",
       "      <td>紫金矿业</td>\n",
       "      <td>988.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>600348.XSHG</td>\n",
       "      <td>华阳股份</td>\n",
       "      <td>969.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>002714.XSHE</td>\n",
       "      <td>牧原股份</td>\n",
       "      <td>965.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>300724.XSHE</td>\n",
       "      <td>捷佳伟创</td>\n",
       "      <td>952.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>600809.XSHG</td>\n",
       "      <td>山西汾酒</td>\n",
       "      <td>945.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>002240.XSHE</td>\n",
       "      <td>盛新锂能</td>\n",
       "      <td>942.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>000957.XSHE</td>\n",
       "      <td>中通客车</td>\n",
       "      <td>939.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>000568.XSHE</td>\n",
       "      <td>泸州老窖</td>\n",
       "      <td>902.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>000792.XSHE</td>\n",
       "      <td>盐湖股份</td>\n",
       "      <td>883.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>600111.XSHG</td>\n",
       "      <td>北方稀土</td>\n",
       "      <td>881.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>002176.XSHE</td>\n",
       "      <td>江特电机</td>\n",
       "      <td>880.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>600096.XSHG</td>\n",
       "      <td>云天化</td>\n",
       "      <td>873.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>600150.XSHG</td>\n",
       "      <td>中国船舶</td>\n",
       "      <td>864.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>002050.XSHE</td>\n",
       "      <td>三花智控</td>\n",
       "      <td>863.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>002415.XSHE</td>\n",
       "      <td>海康威视</td>\n",
       "      <td>854.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>688599.XSHG</td>\n",
       "      <td>天合光能</td>\n",
       "      <td>851.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>002156.XSHE</td>\n",
       "      <td>通富微电</td>\n",
       "      <td>851.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>600276.XSHG</td>\n",
       "      <td>恒瑞医药</td>\n",
       "      <td>847.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>601919.XSHG</td>\n",
       "      <td>中远海控</td>\n",
       "      <td>843.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           股票代码   股票名称  累积成交额(亿)\n",
       "1   300750.XSHE   宁德时代   3933.97\n",
       "2   002466.XSHE   天齐锂业   3180.82\n",
       "3   600519.XSHG   贵州茅台   2894.60\n",
       "4   002594.XSHE    比亚迪   2543.09\n",
       "5   600438.XSHG   通威股份   2415.63\n",
       "6   601012.XSHG   隆基绿能   2349.16\n",
       "7   300274.XSHE   阳光电源   2200.87\n",
       "8   000625.XSHE   长安汽车   2089.79\n",
       "9   300059.XSHE   东方财富   2003.17\n",
       "10  000858.XSHE    五粮液   1739.37\n",
       "11  300014.XSHE   亿纬锂能   1524.50\n",
       "12  002460.XSHE   赣锋锂业   1476.90\n",
       "13  002371.XSHE   北方华创   1411.12\n",
       "14  603259.XSHG   药明康德   1398.92\n",
       "15  002475.XSHE   立讯精密   1375.04\n",
       "16  600418.XSHG   江淮汽车   1347.60\n",
       "17  600036.XSHG   招商银行   1332.43\n",
       "18  002129.XSHE  TCL中环   1321.81\n",
       "19  002241.XSHE   歌尔股份   1240.74\n",
       "20  300068.XSHE   南都电源   1200.79\n",
       "21  000629.XSHE   钒钛股份   1200.78\n",
       "22  002738.XSHE   中矿资源   1184.14\n",
       "23  601888.XSHG   中国中免   1160.14\n",
       "24  000821.XSHE   京山轻机   1143.07\n",
       "25  600089.XSHG   特变电工   1123.59\n",
       "26  603799.XSHG   华友钴业   1084.09\n",
       "27  601318.XSHG   中国平安   1073.06\n",
       "28  600256.XSHG   广汇能源   1030.52\n",
       "29  002709.XSHE   天赐材料   1018.43\n",
       "30  600703.XSHG   三安光电    995.57\n",
       "31  601899.XSHG   紫金矿业    988.88\n",
       "32  600348.XSHG   华阳股份    969.73\n",
       "33  002714.XSHE   牧原股份    965.80\n",
       "34  300724.XSHE   捷佳伟创    952.41\n",
       "35  600809.XSHG   山西汾酒    945.68\n",
       "36  002240.XSHE   盛新锂能    942.10\n",
       "37  000957.XSHE   中通客车    939.64\n",
       "38  000568.XSHE   泸州老窖    902.80\n",
       "39  000792.XSHE   盐湖股份    883.73\n",
       "40  600111.XSHG   北方稀土    881.81\n",
       "41  002176.XSHE   江特电机    880.74\n",
       "42  600096.XSHG    云天化    873.13\n",
       "43  600150.XSHG   中国船舶    864.31\n",
       "44  002050.XSHE   三花智控    863.90\n",
       "45  002415.XSHE   海康威视    854.33\n",
       "46  688599.XSHG   天合光能    851.42\n",
       "47  002156.XSHE   通富微电    851.20\n",
       "48  600276.XSHG   恒瑞医药    847.26\n",
       "49  601919.XSHG   中远海控    843.85"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国股市自2022-7-21至2022-10-20累积成交额(亿)(>=60)\u001b[1;31m倒数50\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票代码</th>\n",
       "      <th>股票名称</th>\n",
       "      <th>累积成交额(亿)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>002375.XSHE</td>\n",
       "      <td>亚厦股份</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>002034.XSHE</td>\n",
       "      <td>旺能环境</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>688020.XSHG</td>\n",
       "      <td>方邦股份</td>\n",
       "      <td>60.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600525.XSHG</td>\n",
       "      <td>长园集团</td>\n",
       "      <td>60.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000862.XSHE</td>\n",
       "      <td>银星能源</td>\n",
       "      <td>60.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>300268.XSHE</td>\n",
       "      <td>佳沃食品</td>\n",
       "      <td>60.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>600560.XSHG</td>\n",
       "      <td>金自天正</td>\n",
       "      <td>60.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>601008.XSHG</td>\n",
       "      <td>连云港</td>\n",
       "      <td>60.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>002229.XSHE</td>\n",
       "      <td>鸿博股份</td>\n",
       "      <td>60.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>300494.XSHE</td>\n",
       "      <td>盛天网络</td>\n",
       "      <td>60.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>002089.XSHE</td>\n",
       "      <td>ST新海</td>\n",
       "      <td>60.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>600165.XSHG</td>\n",
       "      <td>新日恒力</td>\n",
       "      <td>60.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>002004.XSHE</td>\n",
       "      <td>华邦健康</td>\n",
       "      <td>60.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>603333.XSHG</td>\n",
       "      <td>尚纬股份</td>\n",
       "      <td>60.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>301078.XSHE</td>\n",
       "      <td>孩子王</td>\n",
       "      <td>60.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>600498.XSHG</td>\n",
       "      <td>烽火通信</td>\n",
       "      <td>60.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>603085.XSHG</td>\n",
       "      <td>天成自控</td>\n",
       "      <td>60.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>000987.XSHE</td>\n",
       "      <td>越秀金控</td>\n",
       "      <td>60.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>000930.XSHE</td>\n",
       "      <td>中粮科技</td>\n",
       "      <td>60.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>002215.XSHE</td>\n",
       "      <td>诺普信</td>\n",
       "      <td>60.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>688659.XSHG</td>\n",
       "      <td>元琛科技</td>\n",
       "      <td>60.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>300527.XSHE</td>\n",
       "      <td>中船应急</td>\n",
       "      <td>60.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>002190.XSHE</td>\n",
       "      <td>成飞集成</td>\n",
       "      <td>60.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>000633.XSHE</td>\n",
       "      <td>合金投资</td>\n",
       "      <td>60.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>002686.XSHE</td>\n",
       "      <td>亿利达</td>\n",
       "      <td>60.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>600707.XSHG</td>\n",
       "      <td>彩虹股份</td>\n",
       "      <td>60.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>300357.XSHE</td>\n",
       "      <td>我武生物</td>\n",
       "      <td>60.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>603309.XSHG</td>\n",
       "      <td>维力医疗</td>\n",
       "      <td>60.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>600459.XSHG</td>\n",
       "      <td>贵研铂业</td>\n",
       "      <td>60.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>002387.XSHE</td>\n",
       "      <td>维信诺</td>\n",
       "      <td>60.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>600215.XSHG</td>\n",
       "      <td>派斯林</td>\n",
       "      <td>60.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>000567.XSHE</td>\n",
       "      <td>海德股份</td>\n",
       "      <td>60.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>002476.XSHE</td>\n",
       "      <td>宝莫股份</td>\n",
       "      <td>60.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>601077.XSHG</td>\n",
       "      <td>渝农商行</td>\n",
       "      <td>60.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>600517.XSHG</td>\n",
       "      <td>国网英大</td>\n",
       "      <td>60.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>002083.XSHE</td>\n",
       "      <td>孚日股份</td>\n",
       "      <td>60.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>000783.XSHE</td>\n",
       "      <td>长江证券</td>\n",
       "      <td>60.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>002795.XSHE</td>\n",
       "      <td>永和智控</td>\n",
       "      <td>60.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>688018.XSHG</td>\n",
       "      <td>乐鑫科技</td>\n",
       "      <td>60.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>300460.XSHE</td>\n",
       "      <td>惠伦晶体</td>\n",
       "      <td>60.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>002712.XSHE</td>\n",
       "      <td>思美传媒</td>\n",
       "      <td>60.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>000403.XSHE</td>\n",
       "      <td>派林生物</td>\n",
       "      <td>60.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>002999.XSHE</td>\n",
       "      <td>天禾股份</td>\n",
       "      <td>61.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>300632.XSHE</td>\n",
       "      <td>光莆股份</td>\n",
       "      <td>61.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>002528.XSHE</td>\n",
       "      <td>英飞拓</td>\n",
       "      <td>61.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>002397.XSHE</td>\n",
       "      <td>梦洁股份</td>\n",
       "      <td>61.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>300518.XSHE</td>\n",
       "      <td>盛讯达</td>\n",
       "      <td>61.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>002451.XSHE</td>\n",
       "      <td>摩恩电气</td>\n",
       "      <td>61.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>301179.XSHE</td>\n",
       "      <td>泽宇智能</td>\n",
       "      <td>61.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>600839.XSHG</td>\n",
       "      <td>四川长虹</td>\n",
       "      <td>61.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           股票代码  股票名称  累积成交额(亿)\n",
       "1   002375.XSHE  亚厦股份     60.00\n",
       "2   002034.XSHE  旺能环境     60.00\n",
       "3   688020.XSHG  方邦股份     60.11\n",
       "4   600525.XSHG  长园集团     60.11\n",
       "5   000862.XSHE  银星能源     60.12\n",
       "6   300268.XSHE  佳沃食品     60.18\n",
       "7   600560.XSHG  金自天正     60.20\n",
       "8   601008.XSHG   连云港     60.21\n",
       "9   002229.XSHE  鸿博股份     60.22\n",
       "10  300494.XSHE  盛天网络     60.23\n",
       "11  002089.XSHE  ST新海     60.25\n",
       "12  600165.XSHG  新日恒力     60.28\n",
       "13  002004.XSHE  华邦健康     60.28\n",
       "14  603333.XSHG  尚纬股份     60.32\n",
       "15  301078.XSHE   孩子王     60.33\n",
       "16  600498.XSHG  烽火通信     60.35\n",
       "17  603085.XSHG  天成自控     60.37\n",
       "18  000987.XSHE  越秀金控     60.41\n",
       "19  000930.XSHE  中粮科技     60.41\n",
       "20  002215.XSHE   诺普信     60.45\n",
       "21  688659.XSHG  元琛科技     60.47\n",
       "22  300527.XSHE  中船应急     60.47\n",
       "23  002190.XSHE  成飞集成     60.48\n",
       "24  000633.XSHE  合金投资     60.49\n",
       "25  002686.XSHE   亿利达     60.51\n",
       "26  600707.XSHG  彩虹股份     60.53\n",
       "27  300357.XSHE  我武生物     60.54\n",
       "28  603309.XSHG  维力医疗     60.55\n",
       "29  600459.XSHG  贵研铂业     60.56\n",
       "30  002387.XSHE   维信诺     60.56\n",
       "31  600215.XSHG   派斯林     60.58\n",
       "32  000567.XSHE  海德股份     60.64\n",
       "33  002476.XSHE  宝莫股份     60.77\n",
       "34  601077.XSHG  渝农商行     60.79\n",
       "35  600517.XSHG  国网英大     60.80\n",
       "36  002083.XSHE  孚日股份     60.82\n",
       "37  000783.XSHE  长江证券     60.88\n",
       "38  002795.XSHE  永和智控     60.95\n",
       "39  688018.XSHG  乐鑫科技     60.95\n",
       "40  300460.XSHE  惠伦晶体     60.97\n",
       "41  002712.XSHE  思美传媒     60.97\n",
       "42  000403.XSHE  派林生物     60.98\n",
       "43  002999.XSHE  天禾股份     61.01\n",
       "44  300632.XSHE  光莆股份     61.06\n",
       "45  002528.XSHE   英飞拓     61.06\n",
       "46  002397.XSHE  梦洁股份     61.17\n",
       "47  300518.XSHE   盛讯达     61.20\n",
       "48  002451.XSHE  摩恩电气     61.20\n",
       "49  301179.XSHE  泽宇智能     61.22\n",
       "50  600839.XSHG  四川长虹     61.23"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "from text import *\n",
    "import time \n",
    "\n",
    "today=datetime.date.today()\n",
    "if today.weekday()>4:\n",
    "    n=2\n",
    "else:\n",
    "    n=1\n",
    "month=today.month-3\n",
    "if month<=0:\n",
    "    month+=12\n",
    "    year=today.year-1\n",
    "else:\n",
    "    year=today.year\n",
    "day=today.day\n",
    "flag=1\n",
    "while flag==1:\n",
    "    try:\n",
    "        start=f'{year}-{month}-{day}'\n",
    "        time.strptime(start,\"%Y-%m-%d\")\n",
    "        flag=0\n",
    "    except:\n",
    "        day-=1      \n",
    "        \n",
    "\n",
    "#start=f'{today.year}-1-1'\n",
    "today=f'{today.year}-{today.month}-{today.day}'\n",
    "yesterday=get_previous_trading_date(today,n,market='cn')\n",
    "if datetime.datetime.now().hour<16:\n",
    "    today=yesterday\n",
    "\n",
    "\n",
    "\n",
    "def getStocks():\n",
    "    \"获取股票数据\"\n",
    "    df = all_instruments(type=\"CS\", market=\"cn\", date=None)\n",
    "    index = df.order_book_id\n",
    "    print(f\"股票数：{len(index)}\")\n",
    "    codes = []\n",
    "    names = []\n",
    "    moneys = []\n",
    "    maxS = [0, 0]\n",
    "    minS = [0, 0]\n",
    "    df = get_price(\n",
    "        index,\n",
    "        start_date=start,\n",
    "        end_date=today,\n",
    "        frequency=\"1d\",\n",
    "        adjust_type=\"pre\",\n",
    "        skip_suspended=False,\n",
    "        market=\"cn\",\n",
    "        fields=\"total_turnover\",\n",
    "        expect_df=False,\n",
    "    )\n",
    "    for code in df.columns:\n",
    "        if df[code][0] > 0 and df[code][-1] > 0:\n",
    "            money = sum(df[code])\n",
    "            name = instruments(code).symbol\n",
    "            moneys.append(round(money / 100000000, 2))\n",
    "            names.append(name)\n",
    "            codes.append(code)\n",
    "    max_money = max(moneys)\n",
    "    index = moneys.index(max_money)\n",
    "    max_name = names[index]\n",
    "    min_money = min(moneys)\n",
    "    index = moneys.index(min_money)\n",
    "    min_name = names[index]\n",
    "    ave = round(sum(moneys) / (len(moneys)), 2)\n",
    "    mid = sorted(moneys)[len(moneys) // 2]\n",
    "    top10 = sum(sorted(moneys)[-1:-11:-1])\n",
    "    top50 = sum(sorted(moneys)[-1:-51:-1])\n",
    "    top100 = sum(sorted(moneys)[-1:-101:-1])\n",
    "\n",
    "    print()\n",
    "    print(f\"中国股市自{start}至{today}共获取到{OKRED}{len(moneys)}{ENDL}支股票的数据，开始分析...\")\n",
    "    print(\"#\" * 50)\n",
    "    print(f\"{OKBLUE}累积成交额{ENDL}{OKRED}最高的{ENDL}是：{OKRED}{max_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}累积成交额{ENDL}{OKRED}最低的{ENDL}是：{OKGREEN}{min_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}累积成交额{ENDL}{OKRED}平均值{ENDL}是：{OKRED}{ave}{ENDC}亿\")\n",
    "    print(f\"{OKBLUE}累积成交额{ENDL}{OKRED}中位数{ENDL}是：{OKGREEN}{mid}{ENDC}亿\")\n",
    "    print(\n",
    "        f\"{OKBLUE}累积成交额{ENDL}{OKRED}前10({10/len(moneys)*100:.2f}%){ENDL}占总成交额：{OKRED}{top10/sum(moneys)*100:.2f}%{ENDC}\"\n",
    "    )\n",
    "    print(\n",
    "        f\"{OKBLUE}累积成交额{ENDL}{OKRED}前50({50/len(moneys)*100:.2f}%){ENDL}占总成交额：{OKRED}{top50/sum(moneys)*100:.2f}%{ENDC}\"\n",
    "    )\n",
    "    print(\n",
    "        f\"{OKBLUE}累积成交额{ENDL}{OKRED}前100({100/len(moneys)*100:.2f}%){ENDL}占总成交额：{OKRED}{top100/sum(moneys)*100:.2f}%{ENDC}\"\n",
    "    )\n",
    "    df = pd.DataFrame(\n",
    "        {\n",
    "            \"股票代码\": codes,\n",
    "            \"股票名称\": names,\n",
    "            \"累积成交额(亿)\": moneys,\n",
    "        }\n",
    "    )\n",
    "    return df\n",
    "\n",
    "def drawPic(df,n=50):\n",
    "    '绘制图表'      \n",
    "    fig=sns.histplot(df['累积成交额(亿)'],bins=500)\n",
    "    fig.get_figure().set_figwidth(6)#设置宽度\n",
    "    fig.get_figure().set_figheight(1.8)#设置高度\n",
    "    plt.title(f'a股自{start}至{today}成交额分布')\n",
    "    plt.show()\n",
    "    #display(df[['成交额(亿)']].describe())\n",
    "    df=df.sort_values(by='累积成交额(亿)')\n",
    "    df1=df[-1:-n:-1]\n",
    "    df1.index=[i for i in range(1,len(df1)+1)]\n",
    "    print(f'中国股市自{start}至{today}累积成交额(亿){OKRED}TOP{n}{ENDL}')\n",
    "    display(df1)    \n",
    "    check=60\n",
    "    df = df.loc[df['累积成交额(亿)'] >= check]\n",
    "    df1=df[0:n]\n",
    "    df1.index=[i for i in range(1,len(df1)+1)]\n",
    "    print(f'中国股市自{start}至{today}累积成交额(亿)(>={check}){OKRED}倒数{n}{ENDL}')\n",
    "    display(df1)\n",
    "df=getStocks()\n",
    "drawPic(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五、近一年累积换手率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "股票数：5068\n",
      "计算中：================================================================================================>100.00%\n",
      "中国股市自2021-10-21至2022-10-20共获取到\u001b[1;31m4455\u001b[0m支股票的数据，开始分析...\n",
      "##################################################\n",
      "\u001b[1;94m总换手率\u001b[0m\u001b[1;31m最高的\u001b[0m是：\u001b[1;31m中青宝\u001b[0m\n",
      "\u001b[1;94m总换手率\u001b[0m\u001b[1;31m最低的\u001b[0m是：\u001b[1;92m建设银行\u001b[0m\n",
      "\u001b[1;94m总换手率\u001b[0m\u001b[1;31m平均值\u001b[0m是：\u001b[1;31m4.6559\u001b[0m\n",
      "\u001b[1;94m总换手率\u001b[0m\u001b[1;31m中位数\u001b[0m是：\u001b[1;92m3.7127\u001b[0m\n",
      "\u001b[1;94m日均换手率\u001b[0m\u001b[1;31m最高的\u001b[0m是：\u001b[1;31m中青宝\u001b[0m\n",
      "\u001b[1;94m日均换手率\u001b[0m\u001b[1;31m最低的\u001b[0m是：\u001b[1;92m建设银行\u001b[0m\n",
      "\u001b[1;94m日均换手率\u001b[0m\u001b[1;31m平均值\u001b[0m是：\u001b[1;31m0.0192\u001b[0m\n",
      "\u001b[1;94m日均换手率\u001b[0m\u001b[1;31m中位数\u001b[0m是：\u001b[1;92m0.0153\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>总换手率</th>\n",
       "      <th>日均换手率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4455.000000</td>\n",
       "      <td>4455.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.655898</td>\n",
       "      <td>0.019160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.532442</td>\n",
       "      <td>0.014537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.000300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0%</th>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.000300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>1.295720</td>\n",
       "      <td>0.005300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>1.904140</td>\n",
       "      <td>0.007800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30%</th>\n",
       "      <td>2.452440</td>\n",
       "      <td>0.010100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40%</th>\n",
       "      <td>3.035880</td>\n",
       "      <td>0.012500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.712700</td>\n",
       "      <td>0.015300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60%</th>\n",
       "      <td>4.467560</td>\n",
       "      <td>0.018400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70%</th>\n",
       "      <td>5.529800</td>\n",
       "      <td>0.022780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>6.856220</td>\n",
       "      <td>0.028200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>9.208080</td>\n",
       "      <td>0.037900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.476900</td>\n",
       "      <td>0.133600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              总换手率        日均换手率\n",
       "count  4455.000000  4455.000000\n",
       "mean      4.655898     0.019160\n",
       "std       3.532442     0.014537\n",
       "min       0.074000     0.000300\n",
       "0%        0.074000     0.000300\n",
       "10%       1.295720     0.005300\n",
       "20%       1.904140     0.007800\n",
       "30%       2.452440     0.010100\n",
       "40%       3.035880     0.012500\n",
       "50%       3.712700     0.015300\n",
       "60%       4.467560     0.018400\n",
       "70%       5.529800     0.022780\n",
       "80%       6.856220     0.028200\n",
       "90%       9.208080     0.037900\n",
       "max      32.476900     0.133600"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x129.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x129.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国股市自2021-10-21至2022-10-20总换手率\u001b[1;31mTOP50\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票代码</th>\n",
       "      <th>股票名称</th>\n",
       "      <th>总换手率</th>\n",
       "      <th>日均换手率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300052.XSHE</td>\n",
       "      <td>中青宝</td>\n",
       "      <td>32.4769</td>\n",
       "      <td>0.1336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>002699.XSHE</td>\n",
       "      <td>ST美盛</td>\n",
       "      <td>25.7285</td>\n",
       "      <td>0.1059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>002432.XSHE</td>\n",
       "      <td>九安医疗</td>\n",
       "      <td>24.9821</td>\n",
       "      <td>0.1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>002205.XSHE</td>\n",
       "      <td>国统股份</td>\n",
       "      <td>24.2750</td>\n",
       "      <td>0.0999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>002207.XSHE</td>\n",
       "      <td>准油股份</td>\n",
       "      <td>23.9361</td>\n",
       "      <td>0.0985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>002639.XSHE</td>\n",
       "      <td>雪人股份</td>\n",
       "      <td>23.8844</td>\n",
       "      <td>0.0983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>300249.XSHE</td>\n",
       "      <td>依米康</td>\n",
       "      <td>23.2622</td>\n",
       "      <td>0.0957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>300534.XSHE</td>\n",
       "      <td>陇神戎发</td>\n",
       "      <td>22.6231</td>\n",
       "      <td>0.0931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>300264.XSHE</td>\n",
       "      <td>佳创视讯</td>\n",
       "      <td>22.5914</td>\n",
       "      <td>0.0930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>300094.XSHE</td>\n",
       "      <td>国联水产</td>\n",
       "      <td>22.4455</td>\n",
       "      <td>0.0924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>000812.XSHE</td>\n",
       "      <td>陕西金叶</td>\n",
       "      <td>21.6451</td>\n",
       "      <td>0.0891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>300479.XSHE</td>\n",
       "      <td>神思电子</td>\n",
       "      <td>21.5578</td>\n",
       "      <td>0.0887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>002104.XSHE</td>\n",
       "      <td>恒宝股份</td>\n",
       "      <td>21.3056</td>\n",
       "      <td>0.0877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>002537.XSHE</td>\n",
       "      <td>海联金汇</td>\n",
       "      <td>21.1990</td>\n",
       "      <td>0.0872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>300338.XSHE</td>\n",
       "      <td>ST开元</td>\n",
       "      <td>20.5999</td>\n",
       "      <td>0.0848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>600313.XSHG</td>\n",
       "      <td>农发种业</td>\n",
       "      <td>20.3950</td>\n",
       "      <td>0.0839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>002374.XSHE</td>\n",
       "      <td>中锐股份</td>\n",
       "      <td>20.3580</td>\n",
       "      <td>0.0838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>300612.XSHE</td>\n",
       "      <td>宣亚国际</td>\n",
       "      <td>20.1021</td>\n",
       "      <td>0.0827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>000665.XSHE</td>\n",
       "      <td>湖北广电</td>\n",
       "      <td>20.0927</td>\n",
       "      <td>0.0827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>002725.XSHE</td>\n",
       "      <td>跃岭股份</td>\n",
       "      <td>20.0535</td>\n",
       "      <td>0.0825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>002828.XSHE</td>\n",
       "      <td>贝肯能源</td>\n",
       "      <td>19.7342</td>\n",
       "      <td>0.0812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>300164.XSHE</td>\n",
       "      <td>通源石油</td>\n",
       "      <td>19.5648</td>\n",
       "      <td>0.0805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>600992.XSHG</td>\n",
       "      <td>贵绳股份</td>\n",
       "      <td>18.7723</td>\n",
       "      <td>0.0773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>300546.XSHE</td>\n",
       "      <td>雄帝科技</td>\n",
       "      <td>18.6962</td>\n",
       "      <td>0.0769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>300081.XSHE</td>\n",
       "      <td>恒信东方</td>\n",
       "      <td>18.6847</td>\n",
       "      <td>0.0769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>600367.XSHG</td>\n",
       "      <td>红星发展</td>\n",
       "      <td>18.5830</td>\n",
       "      <td>0.0765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>002943.XSHE</td>\n",
       "      <td>宇晶股份</td>\n",
       "      <td>18.5661</td>\n",
       "      <td>0.0764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>300688.XSHE</td>\n",
       "      <td>创业黑马</td>\n",
       "      <td>18.5620</td>\n",
       "      <td>0.0764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>002370.XSHE</td>\n",
       "      <td>亚太药业</td>\n",
       "      <td>18.5542</td>\n",
       "      <td>0.0764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>300364.XSHE</td>\n",
       "      <td>中文在线</td>\n",
       "      <td>18.4870</td>\n",
       "      <td>0.0761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>000821.XSHE</td>\n",
       "      <td>京山轻机</td>\n",
       "      <td>18.4417</td>\n",
       "      <td>0.0759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>002657.XSHE</td>\n",
       "      <td>中科金财</td>\n",
       "      <td>18.3382</td>\n",
       "      <td>0.0755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>000965.XSHE</td>\n",
       "      <td>天保基建</td>\n",
       "      <td>18.2073</td>\n",
       "      <td>0.0749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>002316.XSHE</td>\n",
       "      <td>*ST亚联</td>\n",
       "      <td>18.1355</td>\n",
       "      <td>0.0746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>002177.XSHE</td>\n",
       "      <td>*ST御银</td>\n",
       "      <td>18.0044</td>\n",
       "      <td>0.0741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>600405.XSHG</td>\n",
       "      <td>动力源</td>\n",
       "      <td>17.9701</td>\n",
       "      <td>0.0740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>300437.XSHE</td>\n",
       "      <td>清水源</td>\n",
       "      <td>17.9136</td>\n",
       "      <td>0.0737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>000957.XSHE</td>\n",
       "      <td>中通客车</td>\n",
       "      <td>17.7349</td>\n",
       "      <td>0.0730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>300013.XSHE</td>\n",
       "      <td>新宁物流</td>\n",
       "      <td>17.6915</td>\n",
       "      <td>0.0728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>000856.XSHE</td>\n",
       "      <td>冀东装备</td>\n",
       "      <td>17.6525</td>\n",
       "      <td>0.0726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>002031.XSHE</td>\n",
       "      <td>巨轮智能</td>\n",
       "      <td>17.6154</td>\n",
       "      <td>0.0725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>600355.XSHG</td>\n",
       "      <td>精伦电子</td>\n",
       "      <td>17.5372</td>\n",
       "      <td>0.0722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>300165.XSHE</td>\n",
       "      <td>天瑞仪器</td>\n",
       "      <td>17.3831</td>\n",
       "      <td>0.0715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>002349.XSHE</td>\n",
       "      <td>精华制药</td>\n",
       "      <td>17.3269</td>\n",
       "      <td>0.0713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>002235.XSHE</td>\n",
       "      <td>安妮股份</td>\n",
       "      <td>17.3081</td>\n",
       "      <td>0.0712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>300261.XSHE</td>\n",
       "      <td>雅本化学</td>\n",
       "      <td>17.2450</td>\n",
       "      <td>0.0710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>002629.XSHE</td>\n",
       "      <td>仁智股份</td>\n",
       "      <td>17.1979</td>\n",
       "      <td>0.0708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>300556.XSHE</td>\n",
       "      <td>丝路视觉</td>\n",
       "      <td>17.1217</td>\n",
       "      <td>0.0705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>002112.XSHE</td>\n",
       "      <td>三变科技</td>\n",
       "      <td>17.0787</td>\n",
       "      <td>0.0703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           股票代码   股票名称     总换手率   日均换手率\n",
       "1   300052.XSHE    中青宝  32.4769  0.1336\n",
       "2   002699.XSHE   ST美盛  25.7285  0.1059\n",
       "3   002432.XSHE   九安医疗  24.9821  0.1028\n",
       "4   002205.XSHE   国统股份  24.2750  0.0999\n",
       "5   002207.XSHE   准油股份  23.9361  0.0985\n",
       "6   002639.XSHE   雪人股份  23.8844  0.0983\n",
       "7   300249.XSHE    依米康  23.2622  0.0957\n",
       "8   300534.XSHE   陇神戎发  22.6231  0.0931\n",
       "9   300264.XSHE   佳创视讯  22.5914  0.0930\n",
       "10  300094.XSHE   国联水产  22.4455  0.0924\n",
       "11  000812.XSHE   陕西金叶  21.6451  0.0891\n",
       "12  300479.XSHE   神思电子  21.5578  0.0887\n",
       "13  002104.XSHE   恒宝股份  21.3056  0.0877\n",
       "14  002537.XSHE   海联金汇  21.1990  0.0872\n",
       "15  300338.XSHE   ST开元  20.5999  0.0848\n",
       "16  600313.XSHG   农发种业  20.3950  0.0839\n",
       "17  002374.XSHE   中锐股份  20.3580  0.0838\n",
       "18  300612.XSHE   宣亚国际  20.1021  0.0827\n",
       "19  000665.XSHE   湖北广电  20.0927  0.0827\n",
       "20  002725.XSHE   跃岭股份  20.0535  0.0825\n",
       "21  002828.XSHE   贝肯能源  19.7342  0.0812\n",
       "22  300164.XSHE   通源石油  19.5648  0.0805\n",
       "23  600992.XSHG   贵绳股份  18.7723  0.0773\n",
       "24  300546.XSHE   雄帝科技  18.6962  0.0769\n",
       "25  300081.XSHE   恒信东方  18.6847  0.0769\n",
       "26  600367.XSHG   红星发展  18.5830  0.0765\n",
       "27  002943.XSHE   宇晶股份  18.5661  0.0764\n",
       "28  300688.XSHE   创业黑马  18.5620  0.0764\n",
       "29  002370.XSHE   亚太药业  18.5542  0.0764\n",
       "30  300364.XSHE   中文在线  18.4870  0.0761\n",
       "31  000821.XSHE   京山轻机  18.4417  0.0759\n",
       "32  002657.XSHE   中科金财  18.3382  0.0755\n",
       "33  000965.XSHE   天保基建  18.2073  0.0749\n",
       "34  002316.XSHE  *ST亚联  18.1355  0.0746\n",
       "35  002177.XSHE  *ST御银  18.0044  0.0741\n",
       "36  600405.XSHG    动力源  17.9701  0.0740\n",
       "37  300437.XSHE    清水源  17.9136  0.0737\n",
       "38  000957.XSHE   中通客车  17.7349  0.0730\n",
       "39  300013.XSHE   新宁物流  17.6915  0.0728\n",
       "40  000856.XSHE   冀东装备  17.6525  0.0726\n",
       "41  002031.XSHE   巨轮智能  17.6154  0.0725\n",
       "42  600355.XSHG   精伦电子  17.5372  0.0722\n",
       "43  300165.XSHE   天瑞仪器  17.3831  0.0715\n",
       "44  002349.XSHE   精华制药  17.3269  0.0713\n",
       "45  002235.XSHE   安妮股份  17.3081  0.0712\n",
       "46  300261.XSHE   雅本化学  17.2450  0.0710\n",
       "47  002629.XSHE   仁智股份  17.1979  0.0708\n",
       "48  300556.XSHE   丝路视觉  17.1217  0.0705\n",
       "49  002112.XSHE   三变科技  17.0787  0.0703"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国股市自2021-10-21至2022-10-20总换手率(>=0.6)\u001b[1;31m倒数50\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票代码</th>\n",
       "      <th>股票名称</th>\n",
       "      <th>总换手率</th>\n",
       "      <th>日均换手率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>688425.XSHG</td>\n",
       "      <td>铁建重工</td>\n",
       "      <td>0.6023</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600265.XSHG</td>\n",
       "      <td>ST景谷</td>\n",
       "      <td>0.6071</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>002423.XSHE</td>\n",
       "      <td>中粮资本</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600519.XSHG</td>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>0.6143</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>601811.XSHG</td>\n",
       "      <td>新华文轩</td>\n",
       "      <td>0.6163</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>601163.XSHG</td>\n",
       "      <td>三角轮胎</td>\n",
       "      <td>0.6165</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>601881.XSHG</td>\n",
       "      <td>中国银河</td>\n",
       "      <td>0.6182</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>300628.XSHE</td>\n",
       "      <td>亿联网络</td>\n",
       "      <td>0.6197</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>603638.XSHG</td>\n",
       "      <td>艾迪精密</td>\n",
       "      <td>0.6233</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>601512.XSHG</td>\n",
       "      <td>中新集团</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>000410.XSHE</td>\n",
       "      <td>*ST沈机</td>\n",
       "      <td>0.6254</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>600886.XSHG</td>\n",
       "      <td>国投电力</td>\n",
       "      <td>0.6298</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>605099.XSHG</td>\n",
       "      <td>共创草坪</td>\n",
       "      <td>0.6326</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>600177.XSHG</td>\n",
       "      <td>雅戈尔</td>\n",
       "      <td>0.6362</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>601601.XSHG</td>\n",
       "      <td>中国太保</td>\n",
       "      <td>0.6429</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>601326.XSHG</td>\n",
       "      <td>秦港股份</td>\n",
       "      <td>0.6432</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>000800.XSHE</td>\n",
       "      <td>一汽解放</td>\n",
       "      <td>0.6463</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>600050.XSHG</td>\n",
       "      <td>中国联通</td>\n",
       "      <td>0.6502</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>600674.XSHG</td>\n",
       "      <td>川投能源</td>\n",
       "      <td>0.6511</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>600306.XSHG</td>\n",
       "      <td>ST商城</td>\n",
       "      <td>0.6555</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>600482.XSHG</td>\n",
       "      <td>中国动力</td>\n",
       "      <td>0.6577</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>600036.XSHG</td>\n",
       "      <td>招商银行</td>\n",
       "      <td>0.6586</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>600398.XSHG</td>\n",
       "      <td>海澜之家</td>\n",
       "      <td>0.6610</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>688276.XSHG</td>\n",
       "      <td>百克生物</td>\n",
       "      <td>0.6616</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>603992.XSHG</td>\n",
       "      <td>松霖科技</td>\n",
       "      <td>0.6640</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>688183.XSHG</td>\n",
       "      <td>生益电子</td>\n",
       "      <td>0.6642</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>603868.XSHG</td>\n",
       "      <td>飞科电器</td>\n",
       "      <td>0.6660</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>601066.XSHG</td>\n",
       "      <td>中信建投</td>\n",
       "      <td>0.6665</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>000166.XSHE</td>\n",
       "      <td>申万宏源</td>\n",
       "      <td>0.6719</td>\n",
       "      <td>0.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>688065.XSHG</td>\n",
       "      <td>凯赛生物</td>\n",
       "      <td>0.6752</td>\n",
       "      <td>0.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>002399.XSHE</td>\n",
       "      <td>海普瑞</td>\n",
       "      <td>0.6763</td>\n",
       "      <td>0.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>600928.XSHG</td>\n",
       "      <td>西安银行</td>\n",
       "      <td>0.6790</td>\n",
       "      <td>0.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>600655.XSHG</td>\n",
       "      <td>豫园股份</td>\n",
       "      <td>0.6840</td>\n",
       "      <td>0.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>603392.XSHG</td>\n",
       "      <td>万泰生物</td>\n",
       "      <td>0.6867</td>\n",
       "      <td>0.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>600167.XSHG</td>\n",
       "      <td>联美控股</td>\n",
       "      <td>0.6953</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>688319.XSHG</td>\n",
       "      <td>欧林生物</td>\n",
       "      <td>0.6979</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>601992.XSHG</td>\n",
       "      <td>金隅集团</td>\n",
       "      <td>0.7023</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>601727.XSHG</td>\n",
       "      <td>上海电气</td>\n",
       "      <td>0.7050</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>605296.XSHG</td>\n",
       "      <td>神农集团</td>\n",
       "      <td>0.7061</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>600871.XSHG</td>\n",
       "      <td>石化油服</td>\n",
       "      <td>0.7062</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>688585.XSHG</td>\n",
       "      <td>上纬新材</td>\n",
       "      <td>0.7064</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>000553.XSHE</td>\n",
       "      <td>安道麦A</td>\n",
       "      <td>0.7071</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>688571.XSHG</td>\n",
       "      <td>杭华股份</td>\n",
       "      <td>0.7103</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>600845.XSHG</td>\n",
       "      <td>宝信软件</td>\n",
       "      <td>0.7106</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>601336.XSHG</td>\n",
       "      <td>新华保险</td>\n",
       "      <td>0.7147</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>600956.XSHG</td>\n",
       "      <td>新天绿能</td>\n",
       "      <td>0.7166</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>002841.XSHE</td>\n",
       "      <td>视源股份</td>\n",
       "      <td>0.7218</td>\n",
       "      <td>0.0030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>601901.XSHG</td>\n",
       "      <td>方正证券</td>\n",
       "      <td>0.7225</td>\n",
       "      <td>0.0030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>600515.XSHG</td>\n",
       "      <td>海航基础</td>\n",
       "      <td>0.7278</td>\n",
       "      <td>0.0030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>601228.XSHG</td>\n",
       "      <td>广州港</td>\n",
       "      <td>0.7285</td>\n",
       "      <td>0.0030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           股票代码   股票名称    总换手率   日均换手率\n",
       "1   688425.XSHG   铁建重工  0.6023  0.0025\n",
       "2   600265.XSHG   ST景谷  0.6071  0.0025\n",
       "3   002423.XSHE   中粮资本  0.6111  0.0025\n",
       "4   600519.XSHG   贵州茅台  0.6143  0.0025\n",
       "5   601811.XSHG   新华文轩  0.6163  0.0025\n",
       "6   601163.XSHG   三角轮胎  0.6165  0.0025\n",
       "7   601881.XSHG   中国银河  0.6182  0.0025\n",
       "8   300628.XSHE   亿联网络  0.6197  0.0026\n",
       "9   603638.XSHG   艾迪精密  0.6233  0.0026\n",
       "10  601512.XSHG   中新集团  0.6250  0.0026\n",
       "11  000410.XSHE  *ST沈机  0.6254  0.0026\n",
       "12  600886.XSHG   国投电力  0.6298  0.0026\n",
       "13  605099.XSHG   共创草坪  0.6326  0.0026\n",
       "14  600177.XSHG    雅戈尔  0.6362  0.0026\n",
       "15  601601.XSHG   中国太保  0.6429  0.0026\n",
       "16  601326.XSHG   秦港股份  0.6432  0.0026\n",
       "17  000800.XSHE   一汽解放  0.6463  0.0027\n",
       "18  600050.XSHG   中国联通  0.6502  0.0027\n",
       "19  600674.XSHG   川投能源  0.6511  0.0027\n",
       "20  600306.XSHG   ST商城  0.6555  0.0027\n",
       "21  600482.XSHG   中国动力  0.6577  0.0027\n",
       "22  600036.XSHG   招商银行  0.6586  0.0027\n",
       "23  600398.XSHG   海澜之家  0.6610  0.0027\n",
       "24  688276.XSHG   百克生物  0.6616  0.0027\n",
       "25  603992.XSHG   松霖科技  0.6640  0.0027\n",
       "26  688183.XSHG   生益电子  0.6642  0.0027\n",
       "27  603868.XSHG   飞科电器  0.6660  0.0027\n",
       "28  601066.XSHG   中信建投  0.6665  0.0027\n",
       "29  000166.XSHE   申万宏源  0.6719  0.0028\n",
       "30  688065.XSHG   凯赛生物  0.6752  0.0028\n",
       "31  002399.XSHE    海普瑞  0.6763  0.0028\n",
       "32  600928.XSHG   西安银行  0.6790  0.0028\n",
       "33  600655.XSHG   豫园股份  0.6840  0.0028\n",
       "34  603392.XSHG   万泰生物  0.6867  0.0028\n",
       "35  600167.XSHG   联美控股  0.6953  0.0029\n",
       "36  688319.XSHG   欧林生物  0.6979  0.0029\n",
       "37  601992.XSHG   金隅集团  0.7023  0.0029\n",
       "38  601727.XSHG   上海电气  0.7050  0.0029\n",
       "39  605296.XSHG   神农集团  0.7061  0.0029\n",
       "40  600871.XSHG   石化油服  0.7062  0.0029\n",
       "41  688585.XSHG   上纬新材  0.7064  0.0029\n",
       "42  000553.XSHE   安道麦A  0.7071  0.0029\n",
       "43  688571.XSHG   杭华股份  0.7103  0.0029\n",
       "44  600845.XSHG   宝信软件  0.7106  0.0029\n",
       "45  601336.XSHG   新华保险  0.7147  0.0029\n",
       "46  600956.XSHG   新天绿能  0.7166  0.0029\n",
       "47  002841.XSHE   视源股份  0.7218  0.0030\n",
       "48  601901.XSHG   方正证券  0.7225  0.0030\n",
       "49  600515.XSHG   海航基础  0.7278  0.0030\n",
       "50  601228.XSHG    广州港  0.7285  0.0030"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "from text import *\n",
    "import time\n",
    "\n",
    "today = datetime.date.today()\n",
    "if today.weekday() > 4:\n",
    "    n = 2\n",
    "else:\n",
    "    n = 1\n",
    "year = today.year - 1\n",
    "month = today.month\n",
    "day = today.day\n",
    "flag = 1\n",
    "while flag == 1:\n",
    "    try:\n",
    "        start = f\"{year}-{month}-{day}\"\n",
    "        time.strptime(start, \"%Y-%m-%d\")\n",
    "        flag = 0\n",
    "    except:\n",
    "        day -= 1\n",
    "\n",
    "\n",
    "# start=f'{today.year}-1-1'\n",
    "today = f\"{today.year}-{today.month}-{today.day}\"\n",
    "yesterday = get_previous_trading_date(today, n, market=\"cn\")\n",
    "if datetime.datetime.now().hour < 16:\n",
    "    today = yesterday\n",
    "\n",
    "\n",
    "def getStocks():\n",
    "    \"获取股票数据\"\n",
    "    sh = index_components(\n",
    "        \"000001.XSHG\", date=None, start_date=start, end_date=today, market=\"cn\"\n",
    "    )\n",
    "    sz = index_components(\n",
    "        \"399001.XSHE\", date=None, start_date=start, end_date=today, market=\"cn\"\n",
    "    )\n",
    "    keys = sh.keys()\n",
    "    key = list(keys)[-1]\n",
    "    index = sh[key] + sz[key]\n",
    "    df = all_instruments(type=\"CS\", market=\"cn\", date=None)\n",
    "    index = df.order_book_id\n",
    "    print(f\"股票数：{len(index)}\")\n",
    "\n",
    "    codes = []\n",
    "    names = []\n",
    "    datas = []\n",
    "    aves = []\n",
    "\n",
    "    df = get_price(\n",
    "        index,\n",
    "        start_date=start,\n",
    "        end_date=today,\n",
    "        frequency=\"1d\",\n",
    "        adjust_type=\"pre\",\n",
    "        skip_suspended=False,\n",
    "        market=\"cn\",\n",
    "        fields=\"volume\",\n",
    "        expect_df=False,\n",
    "    )\n",
    "    df2 = get_shares(index, fields=\"total\", expect_df=False)\n",
    "    c = 1\n",
    "    for code in df.columns:\n",
    "        p = c / len(df.columns) * 100\n",
    "        c += 1\n",
    "        string = \"计算中：\" + \"=\" * int(c / len(index) * 100) + f\">{p:.2f}%\"\n",
    "        print(string, end=\"\\r\")\n",
    "        if df[code][0] > 0 and df[code][-1] > 0:\n",
    "            name = instruments(code).symbol\n",
    "            total = df2[code][-1]\n",
    "            data = sum(df[code])\n",
    "            datas.append(round(data / total, 4))\n",
    "            aves.append(round(data / total / len(df[code]), 4))\n",
    "            names.append(name)\n",
    "            codes.append(code)\n",
    "\n",
    "    max_data = max(datas)\n",
    "    index = datas.index(max_data)\n",
    "    max_name = names[index]\n",
    "    min_data = min(datas)\n",
    "    index = datas.index(min_data)\n",
    "    min_name = names[index]\n",
    "    ave = round(sum(datas) / (len(datas)), 4)\n",
    "    mid = sorted(datas)[len(datas) // 2]\n",
    "    mid = round(mid, 4)\n",
    "    top10 = sum(sorted(datas)[-1:-11:-1])\n",
    "    top50 = sum(sorted(datas)[-1:-51:-1])\n",
    "    top100 = sum(sorted(datas)[-1:-101:-1])\n",
    "    print()\n",
    "    print(f\"中国股市自{start}至{today}共获取到{OKRED}{len(datas)}{ENDL}支股票的数据，开始分析...\")\n",
    "    print(\"#\" * 50)\n",
    "    print(f\"{OKBLUE}总换手率{ENDL}{OKRED}最高的{ENDL}是：{OKRED}{max_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}总换手率{ENDL}{OKRED}最低的{ENDL}是：{OKGREEN}{min_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}总换手率{ENDL}{OKRED}平均值{ENDL}是：{OKRED}{ave}{ENDC}\")\n",
    "    print(f\"{OKBLUE}总换手率{ENDL}{OKRED}中位数{ENDL}是：{OKGREEN}{mid}{ENDC}\")\n",
    "\n",
    "    max_data = max(aves)\n",
    "    index = aves.index(max_data)\n",
    "    max_name = names[index]\n",
    "    min_data = min(aves)\n",
    "    index = aves.index(min_data)\n",
    "    min_name = names[index]\n",
    "    ave = round(sum(aves) / (len(aves)), 4)\n",
    "    mid = sorted(aves)[len(aves) // 2]\n",
    "    mid = round(mid, 4)\n",
    "    top10 = sum(sorted(aves)[-1:-11:-1])\n",
    "    top50 = sum(sorted(aves)[-1:-51:-1])\n",
    "    top100 = sum(sorted(aves)[-1:-101:-1])\n",
    "    print(f\"{OKBLUE}日均换手率{ENDL}{OKRED}最高的{ENDL}是：{OKRED}{max_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}日均换手率{ENDL}{OKRED}最低的{ENDL}是：{OKGREEN}{min_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}日均换手率{ENDL}{OKRED}平均值{ENDL}是：{OKRED}{ave}{ENDC}\")\n",
    "    print(f\"{OKBLUE}日均换手率{ENDL}{OKRED}中位数{ENDL}是：{OKGREEN}{mid}{ENDC}\")\n",
    "\n",
    "    df = pd.DataFrame(\n",
    "        {\n",
    "            \"股票代码\": codes,\n",
    "            \"股票名称\": names,\n",
    "            \"总换手率\": datas,\n",
    "            \"日均换手率\": aves,\n",
    "        }\n",
    "    )\n",
    "    return df\n",
    "\n",
    "\n",
    "def drawPic(df, n=50):\n",
    "    \"绘制图表\"\n",
    "    # 分成10档显示中位数统计\n",
    "    display(df.describe(percentiles=[i * 0.1 for i in range(10)]))\n",
    "\n",
    "    fig = sns.histplot(df[\"总换手率\"], bins=500)\n",
    "    fig.get_figure().set_figwidth(6)  # 设置宽度\n",
    "    fig.get_figure().set_figheight(1.8)  # 设置高度\n",
    "    plt.title(f\"a股自{start}至{today}总换手率分布\")\n",
    "    plt.show()\n",
    "\n",
    "    fig = sns.histplot(df[\"日均换手率\"], bins=500)\n",
    "    fig.get_figure().set_figwidth(6)  # 设置宽度\n",
    "    fig.get_figure().set_figheight(1.8)  # 设置高度\n",
    "    plt.title(f\"a股自{start}至{today}日均换手率分布\")\n",
    "    plt.show()\n",
    "\n",
    "    df = df.sort_values(by=\"总换手率\")\n",
    "    df1 = df[-1:-n:-1]\n",
    "    df1.index = [i for i in range(1, len(df1) + 1)]\n",
    "    print(f\"中国股市自{start}至{today}总换手率{OKRED}TOP{n}{ENDL}\")\n",
    "    display(df1)\n",
    "\n",
    "    check = 0.6\n",
    "    df = df.loc[df[\"总换手率\"] >= check]\n",
    "    df1 = df[0:n]\n",
    "    df1.index = [i for i in range(1, len(df1) + 1)]\n",
    "    print(f\"中国股市自{start}至{today}总换手率(>={check}){OKRED}倒数{n}{ENDL}\")\n",
    "    display(df1)\n",
    "\n",
    "\n",
    "df = getStocks()\n",
    "drawPic(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 六、近三月累计换手率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "股票数：5068\n",
      "计算中：===============================================================================================>100.00%\n",
      "中国股市自2022-7-21至2022-10-20共获取到\u001b[1;31m4719\u001b[0m支股票的数据，开始分析...\n",
      "##################################################\n",
      "\u001b[1;94m总换手率\u001b[0m\u001b[1;31m最高的\u001b[0m是：\u001b[1;31m大港股份\u001b[0m\n",
      "\u001b[1;94m总换手率\u001b[0m\u001b[1;31m最低的\u001b[0m是：\u001b[1;92m建设银行\u001b[0m\n",
      "\u001b[1;94m总换手率\u001b[0m\u001b[1;31m平均值\u001b[0m是：\u001b[1;31m1.0707\u001b[0m\n",
      "\u001b[1;94m总换手率\u001b[0m\u001b[1;31m中位数\u001b[0m是：\u001b[1;92m0.7873\u001b[0m\n",
      "\u001b[1;94m日均换手率\u001b[0m\u001b[1;31m最高的\u001b[0m是：\u001b[1;31m大港股份\u001b[0m\n",
      "\u001b[1;94m日均换手率\u001b[0m\u001b[1;31m最低的\u001b[0m是：\u001b[1;92m建设银行\u001b[0m\n",
      "\u001b[1;94m日均换手率\u001b[0m\u001b[1;31m平均值\u001b[0m是：\u001b[1;31m0.0178\u001b[0m\n",
      "\u001b[1;94m日均换手率\u001b[0m\u001b[1;31m中位数\u001b[0m是：\u001b[1;92m0.0131\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>总换手率</th>\n",
       "      <th>日均换手率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4719.000000</td>\n",
       "      <td>4719.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.070714</td>\n",
       "      <td>0.017845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.011319</td>\n",
       "      <td>0.016856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0%</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>0.251220</td>\n",
       "      <td>0.004200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>0.371680</td>\n",
       "      <td>0.006200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30%</th>\n",
       "      <td>0.498800</td>\n",
       "      <td>0.008300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40%</th>\n",
       "      <td>0.632280</td>\n",
       "      <td>0.010500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.787300</td>\n",
       "      <td>0.013100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60%</th>\n",
       "      <td>0.955920</td>\n",
       "      <td>0.015900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70%</th>\n",
       "      <td>1.204060</td>\n",
       "      <td>0.020100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>1.559980</td>\n",
       "      <td>0.026000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>2.175900</td>\n",
       "      <td>0.036220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.481500</td>\n",
       "      <td>0.158000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              总换手率        日均换手率\n",
       "count  4719.000000  4719.000000\n",
       "mean      1.070714     0.017845\n",
       "std       1.011319     0.016856\n",
       "min       0.010000     0.000200\n",
       "0%        0.010000     0.000200\n",
       "10%       0.251220     0.004200\n",
       "20%       0.371680     0.006200\n",
       "30%       0.498800     0.008300\n",
       "40%       0.632280     0.010500\n",
       "50%       0.787300     0.013100\n",
       "60%       0.955920     0.015900\n",
       "70%       1.204060     0.020100\n",
       "80%       1.559980     0.026000\n",
       "90%       2.175900     0.036220\n",
       "max       9.481500     0.158000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x129.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x129.6 with 1 Axes>"
      ]
     },
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      "needs_background": "light"
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国股市自2022-7-21至2022-10-20总换手率\u001b[1;31mTOP100\u001b[0m\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票代码</th>\n",
       "      <th>股票名称</th>\n",
       "      <th>总换手率</th>\n",
       "      <th>日均换手率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>002077.XSHE</td>\n",
       "      <td>大港股份</td>\n",
       "      <td>9.4815</td>\n",
       "      <td>0.1580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000957.XSHE</td>\n",
       "      <td>中通客车</td>\n",
       "      <td>9.3697</td>\n",
       "      <td>0.1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>002828.XSHE</td>\n",
       "      <td>贝肯能源</td>\n",
       "      <td>8.9176</td>\n",
       "      <td>0.1486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>002031.XSHE</td>\n",
       "      <td>巨轮智能</td>\n",
       "      <td>8.7627</td>\n",
       "      <td>0.1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>000821.XSHE</td>\n",
       "      <td>京山轻机</td>\n",
       "      <td>8.3670</td>\n",
       "      <td>0.1395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>000014.XSHE</td>\n",
       "      <td>沙河股份</td>\n",
       "      <td>4.0817</td>\n",
       "      <td>0.0680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>002380.XSHE</td>\n",
       "      <td>科远智慧</td>\n",
       "      <td>4.0685</td>\n",
       "      <td>0.0678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>002173.XSHE</td>\n",
       "      <td>创新医疗</td>\n",
       "      <td>4.0178</td>\n",
       "      <td>0.0670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>300629.XSHE</td>\n",
       "      <td>新劲刚</td>\n",
       "      <td>3.9994</td>\n",
       "      <td>0.0667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>002725.XSHE</td>\n",
       "      <td>跃岭股份</td>\n",
       "      <td>3.9920</td>\n",
       "      <td>0.0665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           股票代码  股票名称    总换手率   日均换手率\n",
       "1   002077.XSHE  大港股份  9.4815  0.1580\n",
       "2   000957.XSHE  中通客车  9.3697  0.1562\n",
       "3   002828.XSHE  贝肯能源  8.9176  0.1486\n",
       "4   002031.XSHE  巨轮智能  8.7627  0.1460\n",
       "5   000821.XSHE  京山轻机  8.3670  0.1395\n",
       "..          ...   ...     ...     ...\n",
       "95  000014.XSHE  沙河股份  4.0817  0.0680\n",
       "96  002380.XSHE  科远智慧  4.0685  0.0678\n",
       "97  002173.XSHE  创新医疗  4.0178  0.0670\n",
       "98  300629.XSHE   新劲刚  3.9994  0.0667\n",
       "99  002725.XSHE  跃岭股份  3.9920  0.0665\n",
       "\n",
       "[99 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中国股市自2022-7-21至2022-10-20日均换手率(>=0.01)\u001b[1;31m倒数100\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票代码</th>\n",
       "      <th>股票名称</th>\n",
       "      <th>总换手率</th>\n",
       "      <th>日均换手率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300538.XSHE</td>\n",
       "      <td>同益股份</td>\n",
       "      <td>0.5973</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>301237.XSHE</td>\n",
       "      <td>和顺科技</td>\n",
       "      <td>0.6001</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300210.XSHE</td>\n",
       "      <td>森远股份</td>\n",
       "      <td>0.6021</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600836.XSHG</td>\n",
       "      <td>上海易连</td>\n",
       "      <td>0.6045</td>\n",
       "      <td>0.0101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>688256.XSHG</td>\n",
       "      <td>寒武纪</td>\n",
       "      <td>0.6077</td>\n",
       "      <td>0.0101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>000739.XSHE</td>\n",
       "      <td>普洛药业</td>\n",
       "      <td>0.6099</td>\n",
       "      <td>0.0102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>600738.XSHG</td>\n",
       "      <td>丽尚国潮</td>\n",
       "      <td>0.6127</td>\n",
       "      <td>0.0102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>000768.XSHE</td>\n",
       "      <td>中航西飞</td>\n",
       "      <td>0.6161</td>\n",
       "      <td>0.0103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>300496.XSHE</td>\n",
       "      <td>中科创达</td>\n",
       "      <td>0.6189</td>\n",
       "      <td>0.0103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>688511.XSHG</td>\n",
       "      <td>天微电子</td>\n",
       "      <td>0.6213</td>\n",
       "      <td>0.0104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           股票代码  股票名称    总换手率   日均换手率\n",
       "1   300538.XSHE  同益股份  0.5973  0.0100\n",
       "2   301237.XSHE  和顺科技  0.6001  0.0100\n",
       "3   300210.XSHE  森远股份  0.6021  0.0100\n",
       "4   600836.XSHG  上海易连  0.6045  0.0101\n",
       "5   688256.XSHG   寒武纪  0.6077  0.0101\n",
       "6   000739.XSHE  普洛药业  0.6099  0.0102\n",
       "7   600738.XSHG  丽尚国潮  0.6127  0.0102\n",
       "8   000768.XSHE  中航西飞  0.6161  0.0103\n",
       "9   300496.XSHE  中科创达  0.6189  0.0103\n",
       "10  688511.XSHG  天微电子  0.6213  0.0104"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "from text import *\n",
    "import time\n",
    "\n",
    "today = datetime.date.today()\n",
    "if today.weekday() > 4:\n",
    "    n = 2\n",
    "else:\n",
    "    n = 1\n",
    "month = today.month - 3\n",
    "if month <= 0:\n",
    "    month += 12\n",
    "    year = today.year - 1\n",
    "else:\n",
    "    year = today.year\n",
    "day = today.day\n",
    "flag = 1\n",
    "while flag == 1:\n",
    "    try:\n",
    "        start = f\"{year}-{month}-{day}\"\n",
    "        time.strptime(start, \"%Y-%m-%d\")\n",
    "        flag = 0\n",
    "    except:\n",
    "        day -= 1\n",
    "\n",
    "\n",
    "today = f\"{today.year}-{today.month}-{today.day}\"\n",
    "yesterday = get_previous_trading_date(today, n, market=\"cn\")\n",
    "if datetime.datetime.now().hour < 16:\n",
    "    today = yesterday\n",
    "# start=yesterday\n",
    "\n",
    "\n",
    "def getStocks():\n",
    "    \"获取股票数据\"\n",
    "    sh = index_components(\n",
    "        \"000001.XSHG\", date=None, start_date=start, end_date=today, market=\"cn\"\n",
    "    )\n",
    "    sz = index_components(\n",
    "        \"399001.XSHE\", date=None, start_date=start, end_date=today, market=\"cn\"\n",
    "    )\n",
    "    keys = sh.keys()\n",
    "    key = list(keys)[-1]\n",
    "    index = sh[key] + sz[key]\n",
    "    df = all_instruments(type=\"CS\", market=\"cn\", date=None)\n",
    "    index = df.order_book_id\n",
    "    print(f\"股票数：{len(index)}\")\n",
    "\n",
    "    codes = []\n",
    "    names = []\n",
    "    datas = []\n",
    "    aves = []\n",
    "\n",
    "    df = get_price(\n",
    "        index,\n",
    "        start_date=start,\n",
    "        end_date=today,\n",
    "        frequency=\"1d\",\n",
    "        adjust_type=\"pre\",\n",
    "        skip_suspended=False,\n",
    "        market=\"cn\",\n",
    "        fields=\"volume\",\n",
    "        expect_df=False,\n",
    "    )\n",
    "    df2 = get_shares(index, fields=\"total\", expect_df=False)\n",
    "    c = 1\n",
    "    for code in df.columns:\n",
    "        p = c / len(df.columns) * 100\n",
    "        c += 1\n",
    "        string = \"计算中：\" + \"=\" * int(c / len(index) * 100) + f\">{p:.2f}%\"\n",
    "        print(string, end=\"\\r\")\n",
    "        if df[code][0] > 0 and df[code][-1] > 0:\n",
    "            name = instruments(code).symbol\n",
    "            total = df2[code][-1]\n",
    "            data = sum(df[code])\n",
    "            datas.append(round(data / total, 4))\n",
    "            aves.append(round(data / total / len(df[code]), 4))\n",
    "            names.append(name)\n",
    "            codes.append(code)\n",
    "\n",
    "    max_data = max(datas)\n",
    "    index = datas.index(max_data)\n",
    "    max_name = names[index]\n",
    "    min_data = min(datas)\n",
    "    index = datas.index(min_data)\n",
    "    min_name = names[index]\n",
    "    ave = round(sum(datas) / (len(datas)), 4)\n",
    "    mid = sorted(datas)[len(datas) // 2]\n",
    "    mid = round(mid, 4)\n",
    "    top10 = sum(sorted(datas)[-1:-11:-1])\n",
    "    top50 = sum(sorted(datas)[-1:-51:-1])\n",
    "    top100 = sum(sorted(datas)[-1:-101:-1])\n",
    "    print()\n",
    "    print(f\"中国股市自{start}至{today}共获取到{OKRED}{len(datas)}{ENDL}支股票的数据，开始分析...\")\n",
    "    print(\"#\" * 50)\n",
    "    print(f\"{OKBLUE}总换手率{ENDL}{OKRED}最高的{ENDL}是：{OKRED}{max_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}总换手率{ENDL}{OKRED}最低的{ENDL}是：{OKGREEN}{min_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}总换手率{ENDL}{OKRED}平均值{ENDL}是：{OKRED}{ave}{ENDC}\")\n",
    "    print(f\"{OKBLUE}总换手率{ENDL}{OKRED}中位数{ENDL}是：{OKGREEN}{mid}{ENDC}\")\n",
    "\n",
    "    max_data = max(aves)\n",
    "    index = aves.index(max_data)\n",
    "    max_name = names[index]\n",
    "    min_data = min(aves)\n",
    "    index = aves.index(min_data)\n",
    "    min_name = names[index]\n",
    "    ave = round(sum(aves) / (len(aves)), 4)\n",
    "    mid = sorted(aves)[len(aves) // 2]\n",
    "    mid = round(mid, 4)\n",
    "    top10 = sum(sorted(aves)[-1:-11:-1])\n",
    "    top50 = sum(sorted(aves)[-1:-51:-1])\n",
    "    top100 = sum(sorted(aves)[-1:-101:-1])\n",
    "    print(f\"{OKBLUE}日均换手率{ENDL}{OKRED}最高的{ENDL}是：{OKRED}{max_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}日均换手率{ENDL}{OKRED}最低的{ENDL}是：{OKGREEN}{min_name}{ENDC}\")\n",
    "    print(f\"{OKBLUE}日均换手率{ENDL}{OKRED}平均值{ENDL}是：{OKRED}{ave}{ENDC}\")\n",
    "    print(f\"{OKBLUE}日均换手率{ENDL}{OKRED}中位数{ENDL}是：{OKGREEN}{mid}{ENDC}\")\n",
    "\n",
    "    df = pd.DataFrame(\n",
    "        {\n",
    "            \"股票代码\": codes,\n",
    "            \"股票名称\": names,\n",
    "            \"总换手率\": datas,\n",
    "            \"日均换手率\": aves,\n",
    "        }\n",
    "    )\n",
    "    return df\n",
    "\n",
    "\n",
    "def drawPic(df, n=100):\n",
    "    \"绘制图表\"\n",
    "    display(df.describe(percentiles=[i * 0.1 for i in range(10)]))\n",
    "\n",
    "    fig = sns.histplot(df[\"总换手率\"], bins=500)\n",
    "    fig.get_figure().set_figwidth(6)  # 设置宽度\n",
    "    fig.get_figure().set_figheight(1.8)  # 设置高度\n",
    "    plt.title(f\"a股自{start}至{today}总换手率分布\")\n",
    "    plt.show()\n",
    "\n",
    "    fig = sns.histplot(df[\"日均换手率\"], bins=500)\n",
    "    fig.get_figure().set_figwidth(6)  # 设置宽度\n",
    "    fig.get_figure().set_figheight(1.8)  # 设置高度\n",
    "    plt.title(f\"a股自{start}至{today}日均换手率分布\")\n",
    "    plt.show()\n",
    "\n",
    "    df = df.sort_values(by=\"总换手率\")\n",
    "    df1 = df[-1:-n:-1]\n",
    "    df1.index = [i for i in range(1, len(df1) + 1)]\n",
    "    print(f\"中国股市自{start}至{today}总换手率{OKRED}TOP{n}{ENDL}\")\n",
    "    display(df1)\n",
    "    check = 0.01\n",
    "    df = df.loc[df[\"日均换手率\"] >= check]\n",
    "    df1 = df[0:n:10]\n",
    "    df1.index = [i for i in range(1, len(df1) + 1)]\n",
    "    print(f\"中国股市自{start}至{today}日均换手率(>={check}){OKRED}倒数{n}{ENDL}\")\n",
    "    display(df1)\n",
    "\n",
    "\n",
    "df = getStocks()\n",
    "drawPic(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 七、指定日期总市值变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                       \u001b[1;92m2021-09-13\u001b[0m                                        \n",
      "####################################################################################################\n",
      "\u001b[1;92m股票总数\u001b[0m：\u001b[1;31m4472\u001b[0m\n",
      "\u001b[1;92m上证指数\u001b[0m：\u001b[1;31m3715.37\u001b[0m\n",
      "\u001b[1;92m市盈率TTM\u001b[0m：\u001b[1;31m14.22\u001b[0m\n",
      "\u001b[1;92m市净率TTM\u001b[0m：\u001b[1;31m1.53\u001b[0m\n",
      "\u001b[1;92m总市值\u001b[0m为:\u001b[1;31m48.36\u001b[0m万亿元                                                                                                                                                                     \n",
      "\n",
      "                                       \u001b[1;92m2022-10-20\u001b[0m                                        \n",
      "####################################################################################################\n",
      "\u001b[1;92m股票总数\u001b[0m：\u001b[1;31m4842\u001b[0m\n",
      "\u001b[1;92m上证指数\u001b[0m：\u001b[1;31m3035.05\u001b[0m\n",
      "\u001b[1;92m市盈率TTM\u001b[0m：\u001b[1;31m11.90\u001b[0m\n",
      "\u001b[1;92m市净率TTM\u001b[0m：\u001b[1;31m1.19\u001b[0m\n",
      "\u001b[1;92m总市值\u001b[0m为:\u001b[1;31m53.14\u001b[0m万亿元                                                                                                                                                                     \n",
      "\n",
      "####################################################################################################\n",
      "自\u001b[1;92m2021-09-13\u001b[0m至\u001b[1;92m2022-10-20\u001b[0m:\n",
      "A股总市值涨幅为\u001b[1;31m4.78\u001b[0m万亿元，增长率为\u001b[1;31m9.89%\u001b[0m。\n",
      "上证指数涨幅为：\u001b[1;92m-18.31\u001b[0m%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "from text import *\n",
    "\n",
    "today=datetime.date.today()\n",
    "if today.weekday()>4:\n",
    "    n=2\n",
    "else:\n",
    "    n=1\n",
    "    \n",
    "start=\"2021-09-13\"\n",
    "#end='2022-09-26'\n",
    "end=today\n",
    "yesterday=get_previous_trading_date(today,n,market='cn')\n",
    "if datetime.datetime.now().hour<20:\n",
    "    end=yesterday\n",
    "def get_market_cap(date):\n",
    "    \"\"\"获取总市值\"\"\"\n",
    "    df=all_instruments(type='CS', market='cn', date=date)\n",
    "    index=df.order_book_id\n",
    "    s=f'{OKGREEN}{date}{ENDL}'\n",
    "    print(f'{s:^100}')\n",
    "    print(\"#\"*100)\n",
    "    print(f'{OKGREEN}股票总数{ENDL}：{OKRED}{len(index)}{ENDL}')\n",
    "    df=get_price('000001.XSHG',start_date=date, end_date=date, frequency='1d',adjust_type='pre', skip_suspended =False, market='cn')\n",
    "    #display(df)\n",
    "    close=df.close[-1]\n",
    "    print(f'{OKGREEN}上证指数{ENDL}：{OKRED}{close:.2f}{ENDL}')\n",
    "    df=index_indicator('000001.XSHG',date,date)\n",
    "    try:\n",
    "        pe_ttm=df.pe_ttm[-1]\n",
    "        pb_ttm=df.pb_ttm[-1]\n",
    "        print(f'{OKGREEN}市盈率TTM{ENDL}：{OKRED}{pe_ttm:.2f}{ENDL}')\n",
    "        print(f'{OKGREEN}市净率TTM{ENDL}：{OKRED}{pb_ttm:.2f}{ENDL}')\n",
    "    except:\n",
    "        pass\n",
    "    market_cap=0\n",
    "    c=0\n",
    "    \n",
    "    df = get_price(\n",
    "        index,\n",
    "        start_date=today,\n",
    "        end_date=today,\n",
    "        frequency=\"1d\",\n",
    "        adjust_type=\"pre\",\n",
    "        skip_suspended=False,\n",
    "        market=\"cn\",\n",
    "        fields=\"close\",\n",
    "        expect_df=False,\n",
    "    )\n",
    "    df2 = get_shares(index, fields=\"total\", expect_df=False)\n",
    "    \n",
    "    for code in df.columns:\n",
    "        c+=1\n",
    "        p=c/len(index)*100\n",
    "        string=f'计算中：{OKRED}'+'='*int(c/len(index)*100)+f'>{p:.0f}%{ENDL}'\n",
    "        print(string,end='\\r')\n",
    "        total=df2[code][-1]\n",
    "        price=df[code][-1]\n",
    "        market_cap+=total*price/100000000/10000\n",
    "    print(' '*200,end='\\r')\n",
    "    print(f'{OKGREEN}总市值{ENDL}为:{OKRED}{market_cap:.2f}{ENDL}万亿元\\n')\n",
    "    #display(df)\n",
    "    return market_cap,close\n",
    "\n",
    "a,c=get_market_cap(start)\n",
    "b,d=get_market_cap(end)\n",
    "print(\"#\"*100)\n",
    "print(f\"自{OKGREEN}{start}{ENDL}至{OKGREEN}{end}{ENDL}:\\nA股总市值涨幅为{OKRED}{b-a:.2f}{ENDL}万亿元，增长率为{OKRED}{(b-a)*100/a:.2f}%{ENDL}。\")\n",
    "print(f\"上证指数涨幅为：{OKGREEN}{(d-c)*100/c:.2f}{ENDL}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 八、近n年涨跌研究"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\u001b[1;31m2021-12-31\u001b[0m收盘到\u001b[1;92m2022-10-20\u001b[0m收盘A股涨跌幅#########################\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>涨幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4560.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.129230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.289242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.691818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.309249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.177814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.018386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.734216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                涨幅\n",
       "count  4560.000000\n",
       "mean     -0.129230\n",
       "std       0.289242\n",
       "min      -0.691818\n",
       "25%      -0.309249\n",
       "50%      -0.177814\n",
       "75%      -0.018386\n",
       "max       2.734216"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\u001b[1;31m2011-10-19\u001b[0m收盘到\u001b[1;92m2022-10-20\u001b[0m收盘A股涨跌幅#########################\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>涨幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2172.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.941587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.461645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.865911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.153405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.295249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.055478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.886081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                涨幅\n",
       "count  2172.000000\n",
       "mean      0.941587\n",
       "std       2.461645\n",
       "min      -0.865911\n",
       "25%      -0.153405\n",
       "50%       0.295249\n",
       "75%       1.055478\n",
       "max      45.886081"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wSEuSJEkzYJBuo2s3buMVH72cneO1TpciSZKkFjNIt9HnfvQrvnz1Rr5z/ZZOlyJJkqQWM0i30RU33wXAt6/b1OFKJEmS1GoG6TYZn6xz5a13A/CtazeTmZ0tSJIkSS1lkG6Tn9x2D2OTdR67/mBuvWsnv9iyo9MlSZIkqYUM0m1y+U1FW8drf+dYAL593eZOliNJkqQWM0i3yWU338kRKxbxiCOXc9TKxQZpSZKkecYg3QaZyeU338UJRy4H4LePWcX3b7yDXRNOgydJkjRfGKTb4Jd3jrJl+zgPL4P0ScesYtdEnUtvurPDlUmSJKlVDNJtcFnZH33CuiJIP+qoFQz0Vfj2tbZ3SJIkzRcG6Ta47Oa7WDrUxzGrlwKwaKCPR91/hX3SkiRJ84hBug0uv/lOHn7EciqV2L3spGNW8fNN29lwz84OViZJkqRWMUi32D2jE1x3+/bdFxo2PPCQEQDnk5YkSZonDNItdsUtRX/0I9bdN0ivXTYEwMZ7ds15TZIkSWq9vk4X0Gv+8OVnseWurXtcv2nZ8XDww3j7619HNSdZuXyE8z54zr1BeqtBWpIkaT4wSB+gLXdt5eSXvWmP6y/5+Wa23HoPT3rp6wH45ofeAhQXHI4M9XG7I9KSJEnzgq0dLbZzvMbwQHXadWuXDbHBIC1JkjQvGKRbbHSixnD/9EF6zcgQt9vaIUmSNC+0PUhHxD9ExFURcVlEnBARR0XE5RFxfUS8oWm7N5bLLo2Ide2uq132NiJ9iCPSkiRJ80Zbe6Qj4pHAgzLzIRFxEvAWYCPwduA/gO9GxOeAHcAZwLHAqcC7gOe2s7Z22TlR4+DFA9OuWzsyxJbtY0zW6vRV/WOAJElSL2t3mtsOLI6IAJYBdwEnAV/KzBpwQfn8ZODCctmFwGPaXFfb7L1Heph6wubtY3NclSRJklqtrSPSmfnTiLgI+D6QFKPOP8rMRn/DJuBQihHpLeVrJiOiGhHVMlj3jIlancl67rFHeu2yQQA23LOLQ5YNz2VpkiRJarG2jkhHxErgd4F/AEaBJ1ME6vtstodl0+3vzLLX+rLNmze3utxZ2zle5P49jUivGSnmknYKPEmSpN7X7taO5wMXZOa/AU+n6I3eFhFD5frVFKPSG4CVABHRB+R0o9GZeW5mnpCZJ6xatarNpR+40Ym9B+nGKLQXHEqSJPW+dgfpUWBt+fhgYCdwEfDUiKgCpwFfK79OKZedClzS5rraojEivah/+o6Z5Yv6GeirOAWeJEnSPNDuOxt+jCIgX0nRvnEW8GPgi8C7gfMy8xqAiPgIcC1Fv/Rpba6rLfbV2hERrB1xCjxJkqT5oN0XG+4CnjXNqkdPs+3ZwNntrKfddjZaO/ZwsSEUU+BtdERakiSp5zmZcQvtHK9RrQT91WmvlQRgzTLvbihJkjQfGKRbaHRikuH+KsW02dNr3N0wc+pEJZIkSeolBukW2jleY9Ee+qMb1owMMT5Z5+7RiTmqSpIkSe3Q7osNF5SdE7Vf64++8sorOf05L9z9/O7Fh8Oax/Gcl7+W4fG7f20fK5ePcN4Hz2l3qZIkSZolg3QL7RyvsXzRwH2WTdTh5Je9affzDffs5JeX3crxT30J91+5+Nf28c0PvaXtdUqSJGn2bO1ooelGpKdaPFh8dtk+NjkXJUmSJKlNDNItMlGrM1HLPc4h3bB4wCAtSZI0HxikW2RfN2NpqFaCRQNVdhikJUmSeppBukUaN2NZtI/WDoAlg31s32WQliRJ6mUG6RbZ3xFpKIO0I9KSJEk9zSDdIvtze/AGg7QkSVLvM0i3yOiBjEgP9TE2WWeiVm93WZIkSWoTg3SL7JyoUY1goLrvX+kSp8CTJEnqeQbpFtk5XmN4oEpE7HPbxlzSztwhSZLUuwzSLbI/N2NpaGzXuEBRkiRJvccg3SKNEen9sajcbnTCIC1JktSrDNItMjo+ud9BesgRaUmSpJ5nkG6RA2ntqFaCwb6KQVqSJKmHGaRbYLJWZ6KW+z0iDcU0eTtt7ZAkSepZBukWOJDbgzcM91cdkZYkSephBukWOJDbgzcsckRakiSppxmkW2D0AG4P3jDcX919N0RJkiT1HoN0C8xkRHp4oMquiRqZ2a6yJEmS1EYG6RaYaY90Arsm6m2qSpIkSe1kkG6BXRM1Ahjo2/9fZ2P02j5pSZKk3mSQboGxyToDfRUiYr9fs2igD/CmLJIkSb3KIN0C45N1Bg9gNBruvTBxdHyyHSVJkiSpzQzSLTBejkgfCFs7JEmSeptBugXGJusM9u3/hYZw74i0rR2SJEm9ySDdAjMZka5WgoG+iiPSkiRJPcog3QJjk7UD7pGGYro8R6QlSZJ6k0G6BWYyIg1Fn/SoI9KSJEk9ySA9S5nJWK3OQHUGQdoRaUmSpJ5lkJ6lyXqSyYxaO4YHqvZIS5Ik9SiD9CyNTRa3+J5Ra0d/EaQzs9VlSZIkqc0M0rM0XgbpA53+DmDRQJXMe8O4JEmSeodBepbGZzMiPdC4u6HtHZIkSb3GID1LY5NFCJ5Rj7Q3ZZEkSepZBulZasWItBccSpIk9R6D9CyN7e6RdkRakiRpITFIz5Ij0pIkSQuTQXqWdk9/N4MbsvRVKgxUK4yOT7a6LEmSJLWZQXqWxieLuxpGxIxe701ZJEmSepNBepbGarUZtXU0eJtwSZKk3mSQnqXxyfqMLjRscERakiSpNxmkZ2lssj6rEelFA45IS5Ik9SKD9CzNekS6vxiRzswWViVJkqR2M0jP0mxHpIcHqtTz3tk/JEmS1BsM0rM0Ptsg3e9c0pIkSb3IID1LRWtHdcav331TFvukJUmSeopBehYma3VqmbO72NARaUmSpJ7U9iAdEQ+OiG9ExI8j4qURcVREXB4R10fEG5q2e2O57NKIWNfuulqh0dc8OIO7GjY4Ii1JktSb+tq584joBz4FPD8zf1QuOw94O/AfwHcj4nPADuAM4FjgVOBdwHPbWVsrjNfKIN2CHulRg7QkSVJPafeI9OOByxshunQS8KXMrAEXlM9PBi4sl10IPKbNdbXEeDkiPZvWjr5qhf5q2NohSZLUY9odpH8DqEXExWXLxm8DizNzV7l+E7AWWA1sAcjMSaAaETO/gm+O7G7tmMXFhuBtwiVJknpRW1s7gCXAIcCTgfsDnwWm3nkk9rDs10TEmcCZAEcccURLC52JVoxIAywa6HNEWpIkqce0e0T6buCizNyVmT8DlgHbI2KoXL+aYlR6A7ASICL6gCzbPO4jM8/NzBMy84RVq1a1ufR9G5ssSpxNjzQUFxw6Ii1JktRb2h2kvww8LSL6IuJo4E7gq8BTy9aN04CvlV+nlMtOBS5pc10t0aoR6eH+KqMTk60oSZIkSXOkra0dmfnziPg08COgBpwF/Az4IvBu4LzMvAYgIj4CXEsxg8dp7ayrVRo90gOzmP4O7h2Rzpza4SJJkqRu1e4eaTLzH4F/nLL40dNsdzZwdrvraaXxyTr91aBSmbale78N91ep573T6UmSJKn7eWfDWRiv1Wfd1gGwyJuySJIk9RyD9CyMTdYZrM5+lr5hbxMuSZLUcwzSszA+2ZoR6cZtwr27oSRJUu8wSM/C2GRt1lPfQdOItEFakiSpZxikZ6HVI9K2dkiSJPUOg/QsjLUoSPdXK/RXwxFpSZKkHmKQnoXxyXpLWjugaO9wRFqSJKl3GKRnqFZPJuvZkhFpKNo7vNhQkiSpdxikZ6hx85TBvtlPfweOSEuSJPUag/QMjTduD97CEWl7pCVJknqHQXqGxiaL0NuqHulF/X3snKiRLdmbJEmS2s0gPUO7R6SrrRuRrtWTevS1ZH+SJElqL4P0DI1NNnqkWzdrB8Bkdagl+5MkSVJ7GaRnqB090gCT1cGW7E+SJEntZZCeobFWB+lyRLpmkJYkSeoJBukZGp9s7fR3iwZs7ZAkSeolBukZGp+sU60E1Uq0ZH+7WzsqjkhLkiT1AoP0DI1N1lp2oSFAf7VCXyUckZYkSeoRBukZGp+st6w/umF4oOrFhpIkST3CID1DY7V6S0ekobjg0IsNJUmSeoNBeoYckZYkSVrYDNIzNDZZZ7Damhk7Ghb1V5ms2CMtSZLUCwzSM9TOEenMbOl+JUmS1HoG6Rkam6y1Pkj3V8lKH6PjtZbuV5IkSa1nkJ6BeiYTtWz9xYblXNJ37hhv6X4lSZLUegbpGZho8e3BGxpB+g6DtCRJUtfbryQYEW+dZtnbWl9ObxjbfXvw1gbpRf19ANy5Y6yl+5UkSVLr9e1tZUSsB44Ffj8ivte0ainw+8BftbG2rjXW5hHpLdsckZYkSep2ew3SFCPWvwUsA57dtHwH8JJ2FdXtxnePSLd4+rsySG/e7oi0JElSt9trkM7M64C/jIjPZ+b356imrjdWK2bVaPWIdH+1QqU+zuZtBmlJkqRut68R6YZKRHwCWAVEY2FmPqEtVXW53SPS1dZfq9k/ucsgLUmS1AP2N0ifB/w5cEMba+kZ7eqRBuir7TRIS5Ik9YD9DdLbM/MLba2kh4y3adYOgL7aLjZt29Xy/UqSJKm19jdIfzcivgh8A9g9XJqZ57Slqi43PlmnElCtxL43PkD9jkhLkiT1hP0N0neUXyNtrKVnjE3WGeyrEtH6IN03uYsd4zV2jE2yeHB/3x5JkiTNtf1Kapn5N+0upJeMT9bb0h8NRWsHwOZtYwZpSZKkLrZfSS0ivgnklMWZmU9sfUndb2yy1pb+aChaO6CYS3rdysVtOYYkSZJmb3+HPF/V9HgYeCLF3Q0XpLkakZYkSVL32t/WjqunLLosIn7MQr1FeK3Osv7+tuy7f7IYkd601Zk7JEmSutn+tnacNWXR0cBE68vpDeOT9ba1dlTrY1Qr4W3CJUmSutz+tnasanqcFDdmeUfry+kN7WztCGDlkgFbOyRJkrrcAc3aERFry+cb21lUN0saI9LVth1j1dJBNhmkJUmSutr+tnY8GvgwsL14GouAMzPzu+0srhvVo4+kPbcHb1i1xCAtSZLU7fa3tePvgadk5q0AEXEo8DngUe0qrFvVKsVFhu3qkQZYvXSIq2/b2rb9S5Ikafb2Nw0ONEJ0aQMw2IZ6ul69DNJtHZFeOsgdO8ap1adO3S1JkqRusb8j0p+NiK8CX6W4Hu53gI+3raouVqsMAO0dkV61dJBaPblzxzirli7IzyuSJEldb69pMCJeGREjmflW4I0UU96NUcwffeMc1Nd1anMwIr26DM/O3CFJktS99jUi/arMfB9AZv4A+EFjRUScB5zfxtq60u7Wjmp7R6QB55KWJEnqYvtKg3ub421B9hzce7Fhe6e/A+9uKEmS1M32FaSviIiXTF0YEWcAt7SnpO42F60djkhLkiR1v321drwc+FRE/BFwOVADHgwMAb+3PweIiFXAz4BnAb+kaAdZBnwkM99ebvNG4EXAXcCzM/OmA/1B5kq90k8E9FejbcdYNNDHksE+e6QlSZK62F6DdGbeAzwlIo4DjqWYseP9mfnfB3CMdwNXl4//Cng78B/AdyPic8AO4Ixy/6cC7wKeeyA/xFyqVQYYqFaIaF+QBu9uKEmS1O329xbh1wDXHOjOI+IUoPl24icBZ2VmLSIuKJ/vBC4sl10IvO9AjzOXapX+tk5917Bq6aAj0pIkSV2sbYkwIhYDfwG8pWnx4sxsXEG3CVgLrAa2AGTmJFCNiPZdyTdL9Up/W/ujG1YtHWSLQVqSJKlrtTMRvg3428wcbVo29VZ9sYdl04qIMyPisoi4bPPmzS0q88AUI9Ltz/mrltjaIUmS1M32986GM/F7wBllL/FK4BRga0QMlaPSqylGpe8GjgeIiD4gM7M23Q4z81zgXIATTjihI/fPrlUG5mREevXIINvHJhkdn2TRQDvfJkmSJM1E2xJhZt4/M9dl5jrg08DzgIuAp5atG6cBXyu/TimXnQpc0q6aWmHOWjuWFFPgbdk23vZjSZIk6cDN9VDnm4EvUszkcV55ESMR8RHgWooZPE6b45oOSK3Sz2Ab72rYsPumLNt2ccTBi9p+PEmSJB2YOQnSmfnipqePnmb92cDZc1HLbGQmtTkakV69dAjAmTskSZK6VPsT4Tyya6IOUZmz6e8Abvc24ZIkSV3JIH0Atu2aANp7e/CGgxcXFzVuuMcgLUmS1I0M0gdg665JgDmZ/q5SCQ5dNsStd+9s+7EkSZJ04AzSB2AuR6QBDls+zK/uMkhLkiR1I4P0AdhWjkjPWZA+aJjbHJGWJEnqSgbpA7Btd2vH3PzaDj1omE3bxhibnPb+NJIkSeogg/QB2D42x60dBw0DsNELDiVJkrqOQfoAzPWI9GHLiyBtn7QkSVL3MUgfgK27JiGTgTm4syHcOyLtzB2SJEndxyB9ALbtmqCSE0TEnBzvkGXDROAFh5IkSV3IIH0Atu2apFqfmLPjDfRVWL100NYOSZKkLmSQPgD/3xMfwJEbvzOnxzz0oGFuu8cgLUmS1G0M0gfg8BWLWDR+55we87CDvCmLJElSNzJId7nipiy7qNez06VIkiSpiUG6yx22fJjxWp0tO8Y6XYokSZKaGKS7XGMKPNs7JEmSuotBussdWgbp2+727oaSJEndxCDd5Xbf3fDu0Q5XIkmSpGYG6S43MtTP0qE+WzskSZK6jEG6Bxx20DC/srVDkiSpqxike0ARpB2RliRJ6iYG6R5w6EHD/Ooue6QlSZK6iUG6Bxy2fJituybZtmui06VIkiSpZJDuAYc5BZ4kSVLXMUj3gHvnkrZPWpIkqVsYpHvA/cq5pG81SEuSJHUNg3QPWLVkkKH+Cjdv2dHpUiRJklQySPeASiU4auUSrt+8vdOlSJIkqWSQ7hFHr17C9ZsM0pIkSd3CIN0j1q9awq/u3snO8VqnS5EkSRIG6Z6xfvUSMuEG2zskSZK6gkG6R6xfvQQwSEuSJHULg3SPWLdyEZWAG+yTliRJ6goG6R4x2FflyIMXO3OHJElSlzBI95CjVy125g5JkqQuYZDuIUevXsIvtuxgslbvdCmSJEkLnkG6h6xftYSJWvLLO0c7XYokSdKC19fpAnRfV155Jac/54XTrhsdPBgOezJ/8GdvZdnor3YvX7l8hPM+eM5clShJkiQM0l1nog4nv+xN064bm6xx/bdv5LDHnsGJ61bsXv7ND71lrsqTJElSydaOHjLYV2XxYJW7dox3uhRJkqQFzyDdY5YvGuDOUYO0JElSpxmke8yKxQPctWOCzOx0KZIkSQuaQbrHrFg0wHitzo6xWqdLkSRJWtAM0j1mxeIBANs7JEmSOswg3WN2B2kvOJQkSeoog3SPWTRQZaivwpbtY50uRZIkaUEzSPeYiGDNsiE23rOr06VIkiQtaAbpHnTIyBB37BhnbNILDiVJkjrFIN2D1i4bAuD2rbZ3SJIkdYpBugetHSmCtO0dkiRJnWOQ7kGD/VVWLBpg41aDtCRJUqe0NUhHxP0i4ksR8dOIuDgiDo2IoyLi8oi4PiLe0LTtG8tll0bEunbWNR+sLS849A6HkiRJndHuEekx4J2Z+UDgfOA1wF8BbweOBU6PiAdGxJHAGeWytwDvanNdPW/tsiF2TtS4Z+dEp0uRJElakNoapDNzc2ZeXD69CVgOnAR8KTNrwAXl85OBC8tlFwKPaWdd88HuPmnbOyRJkjpiLnuknw18BVicmY30twlYC6wGtgBk5iRQjYjq1B1ExJkRcVlEXLZ58+Y5Krs7Hbx4gP5qeMGhJElSh8xJkI6IU4EjgE8DU5t6Yw/Lfk1mnpuZJ2TmCatWrWp9oT2kUgnWLB1yRFqSJKlD2h6kI2I98E7g97O4Mm5bRAyVq1dTjEpvAFaW2/cBWbZ5aC/WLhti87Yx6r8+eC9JkqQ2a/esHUuBjwMvyswN5eKLgKeWrRunAV8rv04pl50KXNLOuuaLtcuGqCfsHFje6VIkSZIWnL427/9VwHrgIxEBMAo8Hfgi8G7gvMy8BiAiPgJcC+ygCNjah8YFh6NDB3e4EkmSpIWnrUE6M98BvGOaVY+eZtuzgbPbWc98s3iwj2XD/WwfWtPpUiRJkhYc72zY49YdvIjtw2vYNWFLuSRJ0lwySPe4+69cTFb6+N4Nd3S6FEmSpAXFIN3jDls+TKU+wdevub3TpUiSJC0oBuke11epsGTnRr7xs00UswtKkiRpLhik54GR0du47Z5dXLNxW6dLkSRJWjAM0vPA0tHbAPjGNZs6XIkkSdLCYZCeB/pru3jI/Zbx9Z/ZJy1JkjRXDNLzxBOOW82PbrmbO7aPdboUSZKkBcEgPU884bjVZMK3r9vc6VIkSZIWBIP0PPEbhy5j9dJBvvyTjZ0uRZIkaUEwSM8TlUrw9Icdyjeu2cTmbbZ3SJIktZtBeh557omHM1lPPvejWztdiiRJ0rxnkJ5H1q9eysOPOIhPXnqLN2eRJElqM4P0PPPcEw/nhs07uOKXd3W6FEmSpHnNID3PPO0hh7J4oMonL72l06VIkiTNawbpeWbxYB9Pe8ihXHDVBraPTXa6HEmSpHnLID0PPefEwxkdr3HBlbd1uhRJkqR5yyA9Dz38iIN4wOol/Mv3bvaiQ0mSpDYxSM9DEcHLTzqan23Yytd/tqnT5UiSJM1LBul56ukPO5QjViziH77xc0elJUmS2sAgPU/1Vyu88uSjuerWe/j2dZs7XY4kSdK809fpAjR7V155Jac/54W/trxOhf7Dn8ZZ77+Qo2+7iCiXr1w+wnkfPGdui5QkSZpnDNLzwEQdTn7Zm6Zdt+rWu/nmtZtZ/8zXccSKRQB880NvmcvyJEmS5iVbO+a5Bx46wpLBPr53wx32SkuSJLWQQXqe66tUeMzRB7Nx6y6uvm1rp8uRJEmaNwzSC8Dxa5dy2EHDfOf6LYyOe7dDSZKkVjBILwARwROOW81Erc4lP9/S6XIkSZLmBYP0ArFi8QAnHLmCazZuY/vQmk6XI0mS1PMM0gvIieuWs2y4n1tXPZK7R8c7XY4kSVJPM0gvIH3VCk950Bom+oZ51b//iMlavdMlSZIk9SyD9AJzyLJhDttyGd+5fgvvvPCaTpcjSZLUswzSC9CKbTfy4v+xjn/6zi/4zOW3drocSZKknuSdDReoNzz1eK7duI0//8xVJPCsR9yv0yVJkiT1FEekF6j+aoUPvegEHnPUwbzu/Ct5/7du8M6HkiRJB8AgvYAtGezjvBefyOkPPZR3ffka/voLV7NrotbpsiRJknqCrR0L3EBfhfc+92GsXTbEuRffyCU/38I7nvFgHn3UwZ0uTZIkqas5Ii0qleD1px7Pv730UdTqyfPO/T5/+Zmr2HDPzk6XJkmS1LUckV6ArrzySk5/zgunXTcSVVYufzCf+GGdT/7wJlZsvYFj8xb+/QPvmeMqJUmSuptBegGaqMPJL3vTXrfZunOCS2+6k59WjuF79aP5i09fxZknHcXRq5bMUZWSJEndzdYOTWtkuJ8nHr+GFz1mHSu23sDnf/wrnvSeb/OKj17Olbfc3enyJEmSOs4grb0aGe7nsDsu57/+8gm88vHr+e4NW3j6+/6L3z/3+1x83WanzJMkSQuWrR3apyuvvJI//MOXAHB49HHnyHounTyW7914B8Njd7D6rp8yMnorUW6/cvkI533wnM4VLEmSNAcM0tqn6XqqJ+t1rtmwjctu7ufmwYNZvqifE9at4Ng1S7n4w2/tUKWSJElzxyCtGemrVPiNw5bxwENHuH7Tdi696U4u+untfP/GO1g68gC2j02yZNDTS5IkzV8mHc1KJYJj1izlAauXcNMdo1x6053ctvIETnzb1/jdB6/l2Y84nEfefwXVSux7Z5IkST3EIK2WiAjuv3Ix6w5exH9+9BweecZLueDK2/jsFb/ioEX9PHb9Sk56wCoefuRy7r9yscFakiT1PIO0WioiWDx2B+94xoN509MeyNevuZ1vXbuZi6/bzH9etQGARQNVHnjICMeuXbp7NHv9miWsWjJIhAFbkiT1BoO0Wm66OyeuAQ7qH2F08GB2Di7np1tX8KMbl1GrDuzeZtlwP8esWcL61UW4fsCaJRy7ZimrlhqwJUlS9zFIq+X2586JAJnJ6HiNr3/qw7zo5a/i55u28/Pbt3PhTzbw8dGJ3dsdvHiA4w5ZyvFrRzjukBGOP2Qp61cvYbCv2s4fQ5Ikaa8M0uqYiGDxYB83/uCrfHbX7buXHw4cWhlk18Aydg0sY+fW5Vxxx0F897plZKU4ZSPrPGDtCMcfUnwdt3YpDzxkxNFrSZI0Z7oqSEfEG4EXAXcBz87MmzpbkebC/o5g1zO5e3SCLdvHuPySr3P48Sdz6S/u5D9+fNvubZYO9XHYQcMcdtAwq0eGGBnqY8lgH8MDVWr1pJZJrVZ+ryeT9WR8ss74ZJ2JWvF9rPze+Jqs1+mvVhjqrzLUX+HgxYOsGRlk9cgQa0aGWDMyyJqlQxy0qN8QL0nSAtI1QToijgTOAI4FTgXeBTy3o0Wpq1QiWLF4gBWLB/jStz7K2ruuYiVwUKWfXQMHsWvgIMb6R/jVHYu4qW8xk9VhapW+3aPYvyaToE5k46tGZJ1K1hnfNcrQQH+xjCSjQkaFevQxWR2iVh38td0N9lU4ZNkQa5cNcciy4fL7EMsXDbBkqI+RoT4G+6pEFD9LtRJUohiZr0QQQAQEQSOPVytBf7XCQLVCf1/xuK8Sew3s9XoyXrv3g8FELYvH5bLJWjLUX2XpUB+LB/tY1F+l4iwqkiQdsK4J0sDJwIWZWYuIC4H3dbogda/9HcUGqNWT9/zJC/iTv/tYGVbvDbB78u6znsmrz/nMHtdP1ursGK+xY2ySHWOT/OhbX+K0ZzyLDffsYuM9O/nhL+7k9q27mKzngf5o+2WgWqG/GvT3VahEMFGG5IlaMdJ+oCr1CfqocfiagxkZ7mfpUD9Lh/oYGeovPgD0VxmoBn3VCv2NY1crVAIyISm+AyS5exkUvfD1elLL4nHjLwOZxXtTL9fXE2p57/NavdhXXyWoVIK+SlCtVKhG0FctPoj0VYoPIdM/L7ZtLK9Wo3htpVjW+OqrVKY8L79X77u+b8r6bv7rQ2Y2vS857fsDjffu3udQfMhrfNirxL3PZ/PzTldPPaevo57FuVAvz5PG91o9qdfZ/bio9b717vE7jZ8niAr3fR40/bxNH2i7+P3tNZnT/5u0h8Xs6V+wPe5nj9vvaf/3nmuN86qe2fTvD7vPvcY20fTfQ6XS9Lhc3vg3ofG4eV3jNb2m+ffd/LvMPW1zn+XN20+/n/sei6b/zou/Ftez/F4+r9WTpUN9rBkZmuFP1B7dFKRXA1sAMnMyIqoRUc3MWofrUo+rVgImx+ivVlq2z75qhWXDFZYN9wPwn5d8iu9vvXb3+oOBFVCMXlcGqFX6qVcGqEdRw8233Mqpf/Da3QGnES6g/MeoKVjUMvnm+edxyGH32z0ynlR3P4ZgkDpDTaPqkXU2briNxz/jRcU/6pUiRFYq3Bu8J4uR6/FanYnJOldcchFbRtdwe6WfWqWfWmWAevk9K3NwYWfWCXL3v7RZm6RSLX6+pEJGQLTuPZytRmhnLv//OM2HlalBuV2aw/XuD6PF32vKc7gzdbXL1BDeHM6ns7fQdmDb70Gb99+qkKo9my6IN+xP8LzPr/wAt99T4J36mm73nBPux98+66GdLuM+Yk//8cy1iPgzYDIz/658fhtweHOQjogzgTPLp8cC1/7ajrrLSsoPB1rwPBcEngcqeB4IPA96yZGZuWq6Fd00Ir0BOB4gIvqAnDoanZnnAud2oLYZiYjLMvOETtehzvNcEHgeqOB5IPA8mC+65++k8DXglIioUlxseEmH65EkSZL2qGtGpDNzY0R8hKJdYwdwWodLkiRJkvaoa4I0QGaeDZzd6TpaqGfaUNR2ngsCzwMVPA8EngfzQtdcbChJkiT1km7qkZYkSZJ6hkG6TSLijRFxfURcGhHrOl2P5lZErIiISyLizeXzoyLi8vKceEOHy9MciIj7RcSXIuKnEXFxRBzqebDwRMTjIuIHEXFVRHwvIo7zPFi4ImJVRGyJiMd7HswPBuk2mHK787dQ3O5cC0RE9AMXAtc1Lf4r4O0U58TpEfHATtSmOTUGvDMzHwicD7wGz4OF6AbgqZn5EIqe2D/F82AhezdwdfnY82AeMEi3x+7bnVMEqsd0uB7NocycoJh1pnkKx5OAL5XnxAXlc81jmbk5My8un94ELMfzYMHJzA2ZuSWKe44fTxGiPA8WoIg4BdgI/KJc5HkwDxik2+M+tzsHquX82FogMnPTlEWLM3NX+XgTsHaOS1JnPRv4Cp4HC1JEvBq4DXgI8H48DxaciFgM/AXFX6kbPA/mAYN0e0ydCiU6UoW6iefEAhURpwJHAJ/G82BBysx/AA6l+DB1Dp4HC9HbgL/NzNGmZZ4H80BXzSM9j+zzdudacLZFxFA5+rCaYvRB81xErAfeCTwlMzMiPA8WqPL9/xjwLfz3YCH6PeCMosOHlcApwFbPg97niHR7eLtzTXUR8NTynDiN4hzRPBYRS4GPAy/KzA3lYs+DBSYijomIxv9rnwL8BM+DBScz75+Z6zJzHcVfp56H58G84Ih0G3i7c03jzcAXKa7YPi8zr+lsOZoDrwLWAx8pR6FGgafjebDQnAKcFRE7KS40OxPYheeB/P/CvOCdDSVJkqQZsLVDkiRJmgGDtCRJkjQDBmlJkiRpBgzSkiRJ0gwYpCVJkqQZMEhL0jwREQftZV3/HJYiSQuCQVqS5oGIOBL4SkQ8LSIui4itEdEXEV+OiGHgy03bfrr8/rqI+M6Ur9/s1M8gSb3GG7JI0jyQmTdHxJnAbZl5QkQ03yXt5cDnI+IJwN8CR0XEZcALgLVN2+3MzB/NXdWS1NsckZakeSAingmcCGREfAc4oWn1CcAHgRXA2cA3KO6yd3+gCvxz+XXy3FUsSb3PEWlJmh/+E7gAuA34bmY+LiJeBTwI+ApwDvDL8qthDHgi0GjnWDp35UpS7zNIS9I8kJm7IuIpwGnAlnLxf1GMPB8HvA1YRTECvftlwLnN+4mIdZl5U9sLlqR5wNYOSZo/ngwcAZweEXcB1wF3AJcAm4GdTdtWgOcBO4BHAxPAg4HfmMuCJamXGaQlaf54FfBvmfk44AcUrRtQzNjxXGCUIjivBDaU615A0Vv9POCxc1qtJPU4g7QkzQMRsR6oZuYdETEATJarhoAjgZOAazLzfGBLZr6kXP8q4HzgNRQXHEqS9pNBWpLmhycB74+IY4DvAp8AahQj0DcCf0ox8jzVvwKPA94L/NGcVCpJ80RkZqdrkCS1QERE+o+6JM0Zg7QkSZI0A7Z2SJIkSTNgkJYkSZJmwCAtSZIkzYBBWpIkSZoBg7QkSZI0AwZpSZIkaQYM0pIkSdIM/P9LPJ/3esIdnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xpRIUEZHYUgmKiEhsqQRFRCS2VIIiIhJbKkEREYktlaCIiMSWSlBERGJLJSgiIrGlEhQRkdhSCYqISGypBEVEJLZUgiIiElsqQRERiS2VoIiIxJZKUEREYkslKCIisVXlO4BIFKSzuQpgCLAq0A/oA/QN/u0DVAM9gO7Bv63fAywOvha1+Wp72ULga2A6MLO2prq5BA9NJNKMtdZ3BpGyls7m+gFDgdWDf/O/Wi8bAlSWMFYLMBNXiEv7+ry2pvqLEmYKBWPM1sDf8i5aF5gGNORddh5wM/AGsD6wM5AEngW6WWsvL0VWKT6VoEggnc2tBGwObJb376bASj5zraC5wDvAW8Dbrf/W1lR/5jVVGTHG3Aacba39pM3lT1prxxpj/g1cBDyG+132xf0uz7LWfl3iuFJgmg6V2Elnc71x5bYZSxbeaj5zFUk/YNvg61vpbG4e3y/HbG1N9TclTxgO44DXgNdxo/4HcdPTEnIaCUrkpbO5QcBOeV9bokVh7bHAu8Dk1q/amuqP/EYqvmAkuBg4zVo7N+/yybip0i+Ap4CJwEBguKZDo0MlKJGTzuaG4vbhtJbeRoDxGiq8viSvFIHXamuqW/xGKqygBN8Gfgwcaa19Ibh8NHAocAnQG9gT2AQYjCvFp6y1r3oJLQWjEpTQS2dzq+Omq1pLb12/iSJtLvA88Cjw39qa6vc951lhrfsEcVPHk4Bx1toPjDFPAm/ippLPwk2XHwysDVwMvGKtDf3jjzuVoIRSOpvbENg/+BqFRnq+vAf8F7gHeC6Mo8T8hTHGmEGti13ySvCS1kUzxpgncPsCT7LWfuopshSQSlBCIZ3NGVzZ7Q/8EDfFKeVlJpDBleIjtTXVCzzn6ZSOVocCU3FTwk/i/uaGAHcBfwT2sdaGrvRlSVodKmUrnc11A8biSm8/3OfxpHytAhwVfC1MZ3OP4kaIk2prqkP1UQJjzOHAesDnuP2FA3H7BA+y1jYaY14GTgOu8JdSCkEjQSk76WxuO+AY4EfAAL9ppAAagfuAfwIPhOFIN8aYKmttU/5/A5XW2sV5l/XLX00q4aQSlLKQzuYGArW48tvUcxwpnunAjcA/a2uq3/MdRkQlKN4E+/l2B47FTXl27/AHJGqew40OJ9TWVM/zHUbiSSUoJZfO5oYBR+P2Ha3tN42UgRzug+j/rK2pfsp3GIkXlaCUTDqbGwf8HPeZPh2xRdrzJu7D6bfU1lQ3+g4j0acSlKJKZ3OVuAUuvwZGeo4j4TENuBy4XlOlUkwqQSmKdDbXAzgSd6SN4X7TSIjVAdcBV9TWVM/wnEUiSCUoBRWce+8k4Be4DxaLFMJi3KrSS7SqVApJJSgFkc7mVsUV30lAf79pJMJacB/Av6i2pvp532Ek/FSCskLS2dxg3Bm3jwN6eo4j8XI/8JvamurXfQeR8FIJynJJZ3O9gNNxC176eY4j8WWBW4FkHM59KIWnEpQuSWdzFcARwAXAMM9xRFo1An8Hzq+tqZ7pO4yEh0pQOi2dze2G+wzXCM9RRJZmHnAhcFltTfUi32Gk/KkEZZnS2dymwJ+BvXxnEemkz4Df4j50rxc5WSqVoCxVOpsbAqRwhzer9BxHZHm8CJxaW1Od9R1EypNKUL4n2O93Gm6/Xx/PcURWVAtwFfC72prq+b7DSHlRCcoS0tncCNwCg208RxEptM+Ak2prqu/3HUTKh0pQAEhnc9XA+bgPvGvqU6JsAvDz2prqr3wHEf90JH+BTGq3Xeon3QiciQpQou8Q4J10Nne07yDin0aCcZZJ9cN95OE4Cy2P9j/wrend19rcdyyREnoCOKG2pvoD30HED5VgXGVS44HrgTVaL2qi8sPbBp26Roup1BneJU4W4VZBX1RbU93sO4yUlkowbjKpXsAVuGN9fs+n3Yc/9VT/fXcubSiRsjAZOKy2pvpz30GkdLRPME4yqU1xn5tqtwAB1myYusPAxhmaGpI4Gg1MSWdz+/kOIqWjEoyLTOoYIAts2tHNDHTbo/7OBqxtKU0wkbKyMjApnc1dFZwYWiJO06FRl0n1xZ2Z+7Cu/Nh7Pbd86oW+u2laVOJsCnBIbU31+76DSPFoJBhlmdRI4GW6WIAAGyx6bVTfpjnaNyJxNgJ4OZ3N/dR3ECkejQSjKpM6Fffxh+We0lloer98x6ATty5cKJHQuhE4WYddix6VYNRkUgOAfwAHFGJzU3pvP/n16u1HF2JbIiH3AXBQbU31a76DSOFoOjRKMqkNcKs/C1KAAFsueG6zXs3zZxVqeyIhtj4wOZ3N7es7iBSOSjAqMqldgOeB4YXcrIEB4+pvn1rIbYqEWB/g7nQ2d5bvIFIYmg6NAvfxh2uBbsW6i+f67PHCB70237ZY2xcJoX/gzkrR6DuILD+VYJhlUhXARUDR35W2YGZMGHhSr8aKnv2LfV8iIfIkcGBtTfVs30Fk+Wg6NKwyqWrgLkpQgAAV2CF71E98vRT3JRIiY4EX0tnchr6DyPJRCYZRJjUUeBoo6eGdBjZ9NXqNxVOnlPI+RUJgOPB8OpvbzXcQ6TqVYNhkUlvhDn82stR3bcDsNPe+lSpt48JS37dImRsAPJjO5k7wHUS6RiUYJpnUONwIcHVfESppWWvs3Htf8HX/ImWsCrgunc2lfAeRzlMJhkUmtS9wD9Dbd5TVGz4ZM7jxi3d85xApU79LZ3OX+A4hnaPVoWGQSR0M3EQRPwLRVU1UvXfroFPXs6aiyncWkTJ1DXBqbU21XmTLmEaC5S6TqgVuoYwKEKCKpg23n/fwZN85RMrYycAN6WxOr7NlTE9OOcukjgf+DVR6TtKu9Ra/vd2Apq8/9p1DpIwdDdyUzuY0Y1KmVILlKpP6OfA3wPiOsjQGeo6ru30umlMX6ciPgQnpbK677yDyfSrBcpRJnQ1c7jtGZ/Swi7YcmZv8tO8cImXuANwxR3v6DiJLUgmWm0zqfOD/fMfois0Wvjiid/Pc6b5ziJS5HwD3pbM57yu85TsqwXKSSf0O+L3vGF1loN9edRN0FnqRZdsNV4TLfbJrKSyVYLlwi2BC+yHb6pZ5NRsvePlZ3zlEQmAX4GatGi0PehLKQSZ1AO5USKG2Te6p9Xu0LJzjO4dICBwIXO07hKgE/cukxuI+Bxj658LA4D3r7njLdw6RkDgxnc2d6ztE3IX+hTfUMqkRwH+ByOwfWKn569FrL3r3Jd85RELiPB102y+VoC+Z1HrAg0A/31EKbfS8B4ZU2Yac7xwiIXFNOps7wHeIuFIJ+pBJDQEeBlb1HaUYKrDDdq2fpNGgSOdUALeks7mdfQeJI5VgqWVS/YAHgHV9RymmVRunjRnS8OmbvnOIhEQP4L/pbG5L30HiRiVYSplUFXA3MMJzkqIzULFr/aSeFba5wXcWkZDojzsx7zq+g8SJSrC0LgF29R2iVKpoHj563gP67KBI5w0BJumoMqWjEiyVTOoI4Oe+Y5TaWovf32Hlxq+m+s4hEiJbADf4DhEXKsFSyKS2wp0RInYMdN+z/o5FWNviO4tIiPw4nc2d7jtEHKgEiy2TGozbD9jLdxRfutuGzWrmP6EzTYh0zcXpbG6s7xBRpxIsJrcQ5nZgTd9RfNtw0ZRt+jbPmeY7h0iIVAG3p7O5NXwHiTKVYHFdAoz1HaIcGKgeP2fCV75ziITMYGCizjpRPCrBYonpQpiO9LILtt4898Jk3zlEQmYUcI3vEFGlEiyGGC+EWZYRC57ZtGdLbpbvHCIhc3Q6mzvRd4goUgkWWibVF7iTGC+E6YiBlcbV3f6B7xwiIXRFOpvb3neIqFEJFt6VgI740IH+zXN2WG/hm1nfOURCpjtwazqbi9xB931SCRaSOznukb5jhMH28x9Zo1vL4rm+c4iEzFrAFb5DRIlKsFAyqdWA633HCIsK7Gq71098zXcOkRA6Mp3N7ec7RFSoBAvnn8BA3yHCZFDTjNHDFn+oIhTpur+ns7lVfIeIApVgIWRSpwDjfccIGwNm57n39q+0TYt8ZxEJmcFoBXpBqARXVCa1EfBn3zHCqpKWtXeee+/zvnOIhNAP09nckb5DhJ1KcEVkUt2Am9DHIVbI0IaPRw9u/PJd3zni7v0pz3L+0WM55/Bt+cMxu/LlJ+99e91d11/A/53U/mTHW9knOHHX1Zn15acALJhfzzW/O5KzDx7JeUfuxLSP3gbguYdu5zeHbsMd15wHQFNjAxefti8tLTq2+gq4Ip3NreU7RJipBFfMucDWvkOEnYGq3esmWmNbmn1nibNVhq3LGZfdyR9vfoGxPzyKB2+5CoBpH73Nx2+/0u7PvPPSU9zzr4sZsubwby+rqurOjnv9mAtvf5X9jjmbSX//EwD/u+c/nP+fyXz01os0LF7E0/feyI4/OIyKCr0MrYB+wL/T2ZzxHSSs9Ne3vDKpUcDZvmNERTcaN95u/iM604RHAwYNoe+AQVhrmf7JewxdZ2NaWlq47cpz2O+Y9v/U19lka874y0R69PruHLDde/Ziyx3HATBotTXJzasDwFpL9x496T9oCPPmzOLFJyax/Z4HFf1xxcBYQKddWk4qweWRSVUA1wKVvqNEyfBFb23bv+mbT33niLOHJ1zDzxPD+Xzqm+x64LE8NvF6Ro75Af0Hrtru7Xv27kOPnks/CfqLj93N5tvuDkC37j1ZOH8u38z4nNeffZiNthrD1b89gpsuPQtrbVEeT4z8MZ3NbeQ7RBipBJfPiWgatOAM9BpXN2GOXhH92fOQk7kiM5XNttud9MWnM+XpB9j1gGOXa1ufvvcaL//vPnY/6AQAxh9+Gn86aTxbbL8HLz4xiYZFC9jtoOPp0asPU994oZAPI456ooNsLxeVYFdlUqsAf/QdI6p62kUjRix4Rmea8MgYww7jD+HpzE18PvVNztp/U/54/B58+OaL/OWsgzu1jXl1X/P3P5zASal/0b2nWze26ahdSN34LAOHrMk2u+zH3DlfM3DImgweujZfT/+smA8pLnZJZ3OH+w4RNirBrvszMMB3iCjbfEF2i97N82b4zhE3Mz774NuVmm88/xijdv0hV97/IZdOeptzrn+E9TYbxemX3M5X0z7iql8fttTtNDc1cc3vjuSgk89n2LqbLHFdS0sLkzM3M2bvI+i38mDmzPyCb6Z/Rr+VBhf1scXIJelsrr/vEGFS5TtAqGRSY4Ba3zGizkD/8XUT3r1r4LFDfGeJk9efe4THJx5Mtx696D9wVY76zVXt3m7xghwzPp+61O1kH7uLqW9kubPuPO689jwAzr72Aar7DuClJyYxYvR4unXvwegfHM7V59TSp//K7Hv0r4vxkOJoCJACfuY7SFgY7X7ppEyqCngV2Mx3lLjI9tnluXd7jdSpY0S6phkYVVtT/arvIGGg6dDO+zkqwJIaNf/J4T1aFs7xnUMkZCrRmSY6TSXYGZnUUOA83zHixmAH71F351u+c4iE0Jh0NneI7xBhoBLsnMuAPr5DxNHKzbNGr7XovZd95xAJoYvT2ZwO6bgMKsFlyaRGA51bFy5FMWbeA6tU2Yac7xwiIbMm8CvfIcqdSnDZLvQdIO4qaFljl/r/vuQ7h0gI/SqdzQ3zHaKcqQQ7kkntA+zoO4bAkMbPx6za8Ln2D4p0TW/gHN8hyplKcGnc8UH/5DuGOAYqdqu/u7uxzY2+s4iEzNHpbG5N3yHKlUpw6Y5AH4koK1U0rT963oPP+M4hEjLdgd/5DlGu9GH59riT5b4PrO05ibRhoeG+lX7y+ZyqVdbznUUkRBqBDWtrqj/2HaTcaCTYvqNRAZYlA93H1d2+AGt1OnKRzuuGRoPtUgm2lUl1RzuSy1p327D5qNyTOgGvSNfUprM5zaC0oRL8vuOANXyHkI5ttPDVrfs013/hO4dIiFQBv/cdotyoBPNlUj2A3/iOIctmoM/4utum+84hEjKHp7O5DXyHKCcqwSXVAkN9h5DO6d2S22azBVmtFhXpvEo0GlyCSnBJv/AdQLpmZG7yxj1bcl/7ziESIj9OZ3Mb+Q5RLlSCrTKpccAmy7ydlBUDK4+ru+N93zlEQqQC+KXvEOVCJfid030HkOXTv3n2DusueutF3zlEQuSwdDY30HeIcqASBMikNgHG+Y4hy2+HeQ8P7dayeJ7vHCIh0RM4xneIcqASdH7hO4CsmArs6rvV3/Wq7xwiIXJyOpur9B3CN5VgJjUId5xQCbnBTdPHDF380Wu+c4iExFrAPr5D+KYShBNxUwMScgbM2Ln39quwTYt9ZxEJiVN9B/At3iXoDpF2iu8YUjiVNK+z89z7nvOdQyQkdktnc7FeFR/vEoRDgSG+Q0hhDWv4aPSgxunv+c4hEhKxHg3GvQRj/eRHlYGqPervbDG2pdl3FpEQOCKdzfXzHcKX+JZgJrUxMMp3DCmObrZx423nPzbZdw6REOgDHOU7hC/xLUF3nFCJsPUXvVHTr2n2p75ziIRAbNdGxLMEM6kK4HDfMaS4DPQaXzdhtu8cIiGwfjqb28F3CB/iWYIwFp0zMBZ62oUjt8w9qxPwiizbj30H8CGuJaip0BjZYsHzW/RunveV7xwiZe7gOB5BJn4lmEn1Bg70HUNKx0D/cXUTPvadQ6TMrQLs6jtEqcWvBOEA3GooiZG+LXO323DhlOd95xApc7GbEo1jCWoqNKZq5j+xbveWhXW+c4iUsQPS2VwP3yFKKV4lmEmtDuzmO4b4YbCr7FE/8Q3fOUTKWH9gL98hSileJQiHEb/HLHkGNs0cs+bi91/xnUOkjMVqSjRuhaAFMcJOc+8fVGkbF/jOIVKm9klnc7FZNxGfEsykBgM1vmOIfxW0rLlL/X9f9J1DpEz1AvbzHaJU4lOCMJ54PV7pwGqNn41epWHa275ziJSp2EyJxqkU9vYdQMqHgcrd6++qMra50XcWkTK0azqbi8XJxuNRgplUFTDOdwwpL1U0bbDjvIee8Z1DpAz1Asb4DlEK8ShB2BG39FdkCessfnf7AU2zPvKdQ6QM7ek7QCnEpQQ1FSrtMtBjXN3t87HW+s4iUmZiMXsWlxJM+A4g5auHXbzFNrmndKYJkSVtns7mhvgOUWzRL8FMah1gY98xpLxtvPCVkdXN9V/6ziFSZiI/JRr9EtQoUDrBQN/xdRO+8J1DpMyoBCPgB74DSDhUt8wftcmCF7VaVOQ7e6SzOeM7RDFFuwQzqQrcylCRTtk69/RGPVoWfOM7h0iZWAUY4TtEMUW7BGEzoJ/vEBIeBgaOq7v9Xd85RMpIpKdEo16C2/sOIOEzoHn2jusuelvHFhVxVIIhtoPvABJOO8x7aPWqlob5vnOIlIHt0tlcpe8QxaISFGlHBXbobvV36byDItAb2NR3iGKJbgm6UycN9x1DwmuVpi/HrN7w8eu+c4iUgW18ByiW6Jag9gfKCjJgdqm/p0+FbVrsO4uIZyrBENJUqKywSprX3Wnu/c/5ziHimUowhFSCUhBrNEzdcWDjjA985xDxaMt0Ntfdd4hiiGYJZlLdiPA7FyktA932rL+j0diWZt9ZRDzpDmzuO0QxRLMEYQvcSSFFCqKbbdykZv7jk33nEPEokgOLqJbgZr4DSPRssOj1Uf2aZn/mO4eIJyrBENGpk6TgDPQeVzfha985RDwZ5TtAMUS1BDfyHUCiqZdduNUWuec0LSpxtGk6m+vpO0ShRbUENRKUotlywXOb9WqeP9N3DpESqyKCu5qiV4KZVHdgXd8xJLoMDBhfN+Ej3zlEPFjPd4BCi14JukOlVfkOIdHWt6V+uw0Wvva87xwiJaYSDAFNhUpJ1Mx/fJ3uLYvqfecQKaHIzbKpBEWWUwV21d3rJ+oA2xInGgmGgFaGSskMavpqzBqLp77qO4dIiWgkGAIaCUpJ7TT3vpUrbeNC3zlESmBY1I4hGsUS3NB3AImXSlrWGlt/7wu+c4iUQAWwju8QhRStEsykBgDVvmNI/Kze+MmYVRqnveM7h0gJRGpKNFolCEN8B5B4MlC5e91dFca2NPnOIlJkkVocoxIUKZAqmjbcYd7DOqSaRJ1GgmVMJSherbv47e0GNM362HcOkSLSSLCMreo7gMSbgZ7j6u6Yi7XWdxaRIonU62zUSlAjQfGuh1205da5p5/2nUOkSFb2HaCQVIIiRbDJwpdGVjfPne47h0gRrOQ7QCGpBEWKwEDf8XW3fe47h0gRDPAdoJCiVoKRmquWcKtumV+z8YKXn/WdQ6TAqtLZXF/fIQolaiWokaCUlW1yT23Qo2XBbN85RAosMvsFo1OCmZQBBvuOIZLPwKA96+7QkWQkaiKzXzA6JQi90cl0pQyt1PzNjmsvevcl3zlECkglWIZ6+A4gsjSj5z0wpKqlYb7vHCIFohIsQypBKVsV2GG7zp30su8cIgWifYJlSCUoZW3VxmljVmv49A3fOUQKQCPBMqQSlLJmoGKX+km9K2xzg+8sIiuov+8AhRKlEuzpO4DIslTRvN6Yuffrs4MSdt18ByiUKJWgRoISCms2fLDjyo1ffeA7h8gKqPQdoFBUgiIlZqDbnvV3NGBti+8sIstJJViGVIISGt1tw6Y18x/XmSYkrFSCZUglKKGy4aLXtunbPGea7xwiyyEyJRilI6xoYYyEioHqfWanp8+vHKCFMhIqCyt6fw1H+o5REFEqQeM7gEhXVdE8fEDzN8N95xDpigHN37zqO0OhRGk6VJ+9EhEpjWbfAQpFJSgiIl2lEixDi30HEBGJCZVgGdJIUESkNCLzGdcolaBGgiIipbHAd4BCiVIJRuZJEREpc3N8ByiUKJXgPN8BRERios53gEKJUgnqrN0iIqWhkWAZUgmKiJRGne8AhRKdEkwkG9EKURGRUtBIsEzV+Q4gIhIDKsEyNcN3ABGRGKjzHaBQolaCX/oOICIScY0kkjnfIQpFJSgiIl0RmalQiF4JfuE7gIhIxNX5DlBIUStBjQRFRIprlu8AhaQSFBGRrvjYd4BCUgmKiEhXfOQ7QCGpBEVEpCs0EixjXxGhkz2KiJQhlWDZSiSbcUUoIiLFoenQMjfNdwARkYhqIGIfRYtiCb7nO4CISER9RiLZ4jtEIUWxBN/yHUBEJKIiNRUKKkEREem8SC2KAZWgiIh0nkowBD4BInOEcxGRMvKB7wCFFr0STCQt8I7vGCIiEfSq7wCFFr0SdDQlKiJSWHNIJDUdGhIqQRGRworcKBBUgiIi0jkv+w5QDCpBERHpjFd8ByiGqJbgZ8A83yFERCJEI8HQcCtEs75jiIhExFxgqu8QxRDNEnSe9h1ARCQipgSDi8iJcglO9h1ARCQiIjkVCtEuweeBJt8hREQiIJKLYiDKJZhI5oApvmOIiETAi74DFEt0S9DRlKiIyIr5gkQysudpjXoJanGMiMiKecx3gGKKeglqJCgismIe9R2gmKJdgonkTCJ46g8RkRJSCYacRoMiIsvnHRLJ6b5DFFMcSvB/vgOIiIRUpEeBEI8SfACI5JEORESKTCUYeonkV+g4oiIiXdUMPOk7RLFFvwSde3wHEBEJmRdJJOf6DlFsKkEREWlP5KdCIS4lmEi+CXzsO4aISIg84DtAKcSjBJ17fQcQEQmJacBzvkOUQpxKUFOiIiKdc2dUzx/YVpxK8CmgzncIEZEQuMN3gFKJTwkmkk3Ag75jiIiUuc+JyVQoxKkEHU2Jioh0LDZToRC/ErwfWOQ7hIhIGbvdd4BSilcJJpL1wH99xxARKVOfAS/4DlFK8SpB51++A4iIlKlYTYVCPEvwEeAL3yFERMpQrKZCIY4lmEi2ADf6jiEiUmY+JZGM1VQoxLEEHU2Jiogs6d++A/gQzxJMJN8nRp+DERFZhmbg775D+BDPEnQ0GhQRce4lkYzlWok4l+AEYKHvECIiZeA63wF8iW8JupNF3uU7hoiIZx8CD/sO4Ut8S9DRlKiIxN31cftsYL64l+DjwDu+Q4iIeLIY+KfvED7FuwTdu5/LfMcQEfFkIonk175D+BTvEnRuBL7yHUJExINrfQfwTSWYSC4GrvYdQ0SkxN4kkZzsO4RvKkHnGmCB7xAiIiX0V98ByoFKECCR/IaYHjJIRGLpC/SaB6gE810GtPgOISJSAn8OdgXFnkqwVSL5ITrhrohE31fA9b5DlAuV4JIu8R1ARKTILiOR1CEjAyrBfInks+jsEiISXd/gFgJKQCX4fRf4DiAiUiRXkEjO9x2inKgE20ok7wee9h1DRKTA6oErfYcoNyrB9p3tO4CISIH9lUSy3neIcqMSbI/bN3iv7xgiIgUyH/iL7xDlSCW4dL9FnxsUkWi4OjgoiLShElyaRPJN4CbfMUREVtA3wIW+Q5QrlWDHfg80+A4hIrICUiSSdb5DlCuVYEcSyU+B63zHEBFZTlPR5wI7pBJctgtwO5VFRMLmbBLJRt8hyplKcFkSyVnApb5jiIh00WQSyYm+Q5S7Kt8BQuIS4BhgmO8gUfPGJ1/x8+vvZ/a8hZy697Z8/NUcbvvfG/Tt1YNVB1TzUOqnS9z+4xlzqL1sInMXLGbDYYO46cwD6d6tigvv+B83PfE6lRWGC4/cg7222YBbn3qdCyY8xX7bbsSffroHDY1N7P2Hm3nw/COoqND7P4m0FuA03yHCQK8EneEOM3SG7xhR09jUzMEXTuDSY8Yz5aqTOXbc1nwzdwH/+sX+TLnq5O8VIMCf75rMceO24bW/nkJ1z+5Mev5dvpozn38+8iqvXXUy9/z+cM78x0MA3PDQy7x8+Ym88N40FjU08q9HX6V21xEqQImDG0gkp/gOEQZ6NeisRPIO4BHfMaLkyTc+ZuvhqzNyvdW+vWz2/IUM6td7qT/Tt1cP5i9yC3brc4tYqU9PenSrpMVamlpavr0MwAI9u3djtZX7MrMux53PvMWPd9q8qI9JpAzMAc7xHSIsNB3aNacCbwDdfQeJgjc/nUllRQU7/fofLGxo5NJjxtPY1MJPLp1IQ2MzR+0+kjMP2HGJnzn7oDHsmfwPtz71OjUbDGOPkcMB+M1BYxh1+t+orDDc+suDAOjZrYq5Cxbx2ax6Hnj5A8Zuvg4HX3Q7Qwf25Yrjf4AxpuSPWaQEziWR/Np3iLDQSLArEsn30TkHC2b+wgamz5nHw6la0mccyAl/vYe7zjmUV644ickXH8PNT71O9r1pS/zMI69+yMp9enHMnlvzxBsfM+3rehY3NvGvR1/ltL23Zd0hK3PnM28BcOb+O7Dz2f9k/NbDufOZt1iwuJFTEjX06dmd59793MdDFim2KcC1vkOEiUqw6y4APvQdIgoG9OnJHiPWo2f3bmy8xmDqFyympcUG1/VijxHr8fbns5b4mQvvfJq/nbovR+4+khP3GsWV9zzPQ69MZfhqK3Pc+G24/dcHc8PDrzBvwWJ2G7Eer155MmsNHsCBO2zKrPoca60ygHWHrMynM+s8PGKRomoEjiKRbPIdJExUgl3lzsh8ou8YUTB+q/W578X3aGpu5sPps1m5Ty9m1ucAaGhs4um3PmXLdYbw4fTZHPin2wDo3aMbM+a4j23OmDOPvr160LtHN2bW5bDWMm/hYpqaW+jRrRKAlpYW/vP4FI7aYySrDKhm2tf1fDqzjsH9q/08aJHiuUiLYbpO+wSXRyL5KJlUGqj1HSXM1h86kB/tuCkjf3YtlRUVXHPy3px8zX18OGM2Bjhi1y0Zud5qvPbRDN7/wu3iuOTocZx6XYbGpmaGDuzHLb/8Ef2rezLxmbfZ4tSrsRYuOnIPundzf9oTn32bvUdtSI9uVfx015EcfNEEBvbtze8O3dnjIxcpuDeBlO8QYWSstb4zhFMmNRB4FxjkO4qIxFozsD2J5Iu+g4SRpkOXlzstyS98xxCR2LtUBbj8VIIrIpG8GbjDdwwRia33gHN9hwgzleCKOwHQensRKbUW4GgSyUW+g4SZSnBFJZJzgCPQWehFpLSuIpF81neIsFMJFkIi+RRwse8YIhIbU4Hf+g4RBSrBwvk98JLvECISeYuBQ0gkF/gOEgUqwUJxJ648DMj5jiIikXY6ieQrvkNEhUqwkBLJD4Cf+44hIpF1G4mkjg1aQCrBQksk/wHobM4iUmjvA8f7DhE1KsHiOB6YtsxbiYh0zkLgIBLJeb6DRI1KsBgSydnAj3A7sEVEVtTPSCRf9x0iilSCxZJIvoD7IL2IyIq4kUTyBt8hokolWEyJ5H+Av/iOISKh9TZwku8QUaYSLL5fAg/7DiEioTMftx9QH7sqIpVgsSWSzcChuCM8iIh0RjPuA/Fv+w4SdSrBUnDHF90P0MouEemMn5FI3u87RByoBEvFvaM7HB1oW0Q6dhmJ5DW+Q8SFSrCUEsl7gaTvGCJStu7GrSOQEjHWWt8Z4ieTugX4se8YIlJWXgTG6sDYpaWRoB9HAg/5DiEiZeMTYB8VYOlpJOhLJtUbeATYwXcUEfGqDthRK0H90EjQF/eOLwHoUEgi8dUIHKgC9Ecl6FMiWQeMAz70nERESq8Z+CmJ5OO+g8SZStC3RHIGsAfwpe8oIlIyLcBRJJK3+g4SdyrBcpBIfgzsCcz2HUVEis4Cx5JI3ug7iKgEy0ci+RbwA0DHCRSJLgucQCL5L99BxFEJlhN3+qUfovMQikTVqSSSf/cdQr6jEiw3ieSjwN5oRCgSNT/X4dDKj0qwHLki3BOo9x1FRAriTBLJK32HkO9TCZarRPJZYBfga99RRGSFnE0ieZnvENI+HTGm3GVSG+OOLDPUdxQR6RILnKUCLG8qwTDIpNYBHgPW8R1FRDqlAThSnwMsfyrBsMikVgceBTb2HUVEOjQX2F9HggkHlWCYZFKDcWefGOk7ioi0azqwF4nka76DSOdoYUyYJJKzcItlnvIdRUS+5z1gBxVguKgEwyaRrMcda/QG31FE5FvP406H9InvINI1mg4Ns0zqF8AlQKXnJCJxdh9wiE6IG04qwbDLpMYDtwH9fUcRiaHrgZNJJJt9B5HloxKMgkxqI+BeYLjvKCIx0QCcRiJ5ve8gsmJUglGRSa0M3IlbOCMixTMNdzb4rO8gsuK0MCYqEsnZuOONXuc7ikiEPQFspQKMDo0EoyiTOgX4C9DNdxSRCPkz8Bvt/4sWlWBUZVLbALei/YQiK2o+cBSJ5J2+g0jhaTo0qhLJl4CtgJt8RxEJsfeAGhVgdGkkGAeZ1BHANUAf31FEQuQO4BgSyXm+g0jxqATjIpMajvs84da+o4iUuTrgVBLJm30HkeLTdGhcJJJTge2BS3HnOROR73sY2FwFGB8aCcZRJjUO+A+wqu8oImViAfBLEslrfAeR0lIJxlUmtSpwNXCg7yginj0L/DSYLZGYUQnGXSa1L64Mh/mOIlJiDcC5wJ/12b/4UgkKZFJ9gT8Cp6D9xBIPrwNHkEi+7juI+KUSlO9kUjXA34EtfEcRKZIckAIuI5Fs9B1G/FMJypIyqSrgLOD3QC/PaUQK6W7gFySSn/kOIuVDJSjty6TWxR2Mew/fUURW0Ee40x7d7zuIlB+VoHQskzoMuBBYw3cUkS7KAf8HXEoiuch3GClPKkFZtkyqF3AGcDY69JqUPwvcDPyaRPJL32GkvKkEpfPcZwv/ABwDVHpOI9KeLG6/33O+g0g4qASl6zKpTYA/Afv5jiISmAKcSyJ5j+8gEi4qQVl+mdR2uH0uYz0nkfh6E/eB97tJJPViJl2mEpQV545FegGwje8oEhvvAOcDt6v8ZEWoBKVwMqndgF+jj1VI8XyAK79bSSRbfIeR8FMJSuFlUiOBXwEHoQU0Uhgf4Y70cqOO8ymFpBKU4nEfuD8TOAodfUaWzxPAlcA9GvlJMagEpfgyqcHAabgDdK/sOY2Uv4XATcCVJJJv+g4j0aYSlNLJpKpxnzE8AdjEcxopP5/hTut1A4nkbN9hJB5UguKH+3jFMcCh6Cg0cfc/4Argv9rfJ6WmEowZY8wAa23dUq7rZq0t7ell3OjwYOBYYIeS3rf4NAO4Dfg3ieRrvsNIfKkEI8oYM8VaO6LNZWsBt+NW2Z0HbIDbR3cfsD9wn7V2t9ImzZNJbYQbHdYCq3jLIcUyD7gLd1zPxzXqk3KgEoyo9kowuHxL4Etr7SxjzKPAeFwJPgg0W2uvKm3SdmRS3YC9gcNx+ar9BpIV0Ij727oJuJdEcqHnPCJLUAlG1FJGggcCKwGTgq/NgEG4EvwaONpa21DSoMvizmCxB26kug8w0G8g6QQLPIMb8d1BIvmN5zwiS6USjChjzNtAfqG9hZtqvA+4DBhrrf2VMeZU3FFeHgpu909r7bMlDdtZmVQlMAb4YfC1ls84soS5wKPAA8CDJJLTPOcR6RSVYER1MB1aiRtRbWCtvdgYMxK4HliMO/7na9ba6SUNu7zckWn2xxXi5n7DxNJruKnOB4BnSSRLu6hKpABUghHVQQnuBayPW5G5KTAMmAi8CtxprX25lDkLxp3rcCdg5+BrU8B4zRQ99cAjfDfa0wlrJfRUghHVQQlmgFpr7TfGmAdxC1Duw50SKWGt/VVpkxZJJjUQN3W6M64cRwAVPiOF0EfAC8Dzwb8vk0g2+Y0kUlhVvgNI6RhjhgOVQQF2B1pf0Hri9q/tbIxZ11r7kbeQheIWY0wKviCT6g+MxhXjVsAWwKp+wpWletxZ2V/49iuRnOU3kkjxaSQYEcaYbYAblnGzu3Fn4H4HuAW4HLeCr3UkCDDUWjuhOCnLjDum6Ra4/Ymt/25KtA/23QJ8CrwPvIebBn8BeFfn5ZM4UgnGjDHGWD3pS5dJVQDD+a4U1wHWwO07HYYbNYfBLL4ruvfzvqaSSC72GUyknKgERbrCjR6H8V0x5v87EHcc1NavQhfmPFy5zQJmtvP9zODrYxLJugLft0gkqQRFisV9rrG1EKtZsiB74D5U3tLmqwH3cZXFwKLg34XA1ySSi0r8CEQiTyUoIiKxpSXjIiISWypBERGJLZWgiIjElkpQRERiSyUoIiKxpRIUEZHYUgmKiEhsqQRFRCS2VIIiIhJbKkEREYktlaCIiMSWSlBERGJLJSgiIrGlEhQRkdhSCYqISGypBEVEJLZUgiIiElsqQRERiS2VoIiIxJZKUEREYkslKCIisaUSFBGR2FIJiohIbKkERUQktlSCIiISWypBERGJLZWgiIjElkpQRERiSyUoIiKxpRIUEZHYUgmKiEhsqQRFRCS2VIIiIhJbKkEREYktlaCIiMSWSlBERGJLJSgiIrGlEhQRkdhSCYqISGypBEVEJLZUgiIiElsqQRERiS2VoIiIxJZKUEREYkslKCIisaUSFBGR2FIJiohIbKkERUQktlSCIiISW/8P5mUFMCrO7YUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\u001b[1;31m2010-10-19\u001b[0m收盘到\u001b[1;92m2022-10-20\u001b[0m收盘A股涨跌幅#########################\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>涨幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1868.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.658099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.343983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.879509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.310687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.042260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.703308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.551164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                涨幅\n",
       "count  1868.000000\n",
       "mean      0.658099\n",
       "std       2.343983\n",
       "min      -0.879509\n",
       "25%      -0.310687\n",
       "50%       0.042260\n",
       "75%       0.703308\n",
       "max      39.551164"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uS5IkSZqMpk5/FxHPBZ6amSdFxBnA+4G1wAeBrwA3RcSXgW3AecBy4GzgUuD8ZtYmSZIkTUazR6S3An0REcA84DHgDOC6zBwCri3vnwmsLJetBE5rcl2SJEnSpDR1RDozfxYR1wM/AJJi1PmHmdlfbrIOOJxiRHpD+ZjBiKhGRLUM1rtFxIXAhQBLly5tZumSJEnSXjV1RDoiFgIvAz4GbAdeQhGon7DZGMv2kJmXZ+bJmXnyokWjXqlRkiRJmhbNvkT464BrM/NfIuKrwM+BLRHRW45KL6YYld4IPBkgImpAjhyNliRJktpJs4P0duAp5e1DgB3A9cA5EXE1cC7wRoog/c6IeB/Flw1vbHJdE/bmt13Ehsc2j3v7hfPncsUnLmtiRZIkSWqFZgfpzwBnRcQdFO0bFwG3A9cAHwKuyMzVABFxJXAnRb/0uU2ua8I2PLaZMy9437i3//Yn39/EaiRJktQqzf6yYT/wqlFWnTrKtiuAFc2sR5IkSZoqXtlQkiRJmgCDtCRJkjQBBmlJkiRpAgzSkiRJ0gQYpCVJkqQJMEhLkiRJE2CQliRJkibAIC1JkiRNgEFakiRJmgCDtCRJkjQBBmlJkiRpAgzSkiRJ0gQYpCVJkqQJMEhLkiRJE2CQliRJkibAIC1JkiRNgEFakiRJmgCDtCRJkjQBBmlJkiRpAgzSkiRJ0gQYpCVJkqQJMEg30Y6BIR6ZcxyZ2epSJEmSNMUM0k30g189woOLnssdD2xqdSmSJEmaYk0N0hFxcUTc3vDTHxHHRsStEXF3RFzcsO17y2W3RMSyZtY1HXYMDPGzhzYDRaCWJEnSzNLUIJ2ZH8zMZ2bmM4HXA9cD7wE+CCwHXhERT4mIo4HzymXvBy5tZl3T4ccPbGJwOKkO9RukJUmSZqDpbO14C/DPwBnAdZk5BFxb3j8TWFkuWwmcNo11TbnBoWFuv38jRx8ym4O33sctv36UgaHhVpclSZKkKTQtQToiuoBzgK8CfZnZX65aBywBFgMbADJzEKhGRHU6amuG1Wu3sGNgiGcvnU9f/zq27RriJw/aJy1JkjSTTNeI9LnAtzJzJzByCosYY9keIuLCiFgVEavWr1/fhDInLzO57b7HWDSnhyPnz+KgHesA+MGvHm1xZZIkSZpK0xWk3wz83/L2lojoLW8vphiVXgMsBIiIGpBlm8cTZOblmXlyZp68aNGiaSh7/937yHYe2z7As5YeTERQG97JkxYfZJ+0JEnSDNP0IB0RhwHHZOYPykXXA+eUrRvnAjeUP2eVy84Gbmx2Xc2yZlM/ATxp8Zzdy0499hBuucc+aUmSpJlkOkak3wh8tuH+JcAfA78AvpqZqzNzLXAlcCfwl8CfTENdTbFl5wB9PTWqlce7U0499hC27xrix/ZJS5IkzRi1Zj9BZl464v564NRRtlsBrGh2Pc22pX+QOb1PPKynHLsAKOaTftbS+a0oS5IkSVPMKxtOsdGC9MKDejjh0IP8wqEkSdIMYpCeQpnJ1p2DzOnt2mPdqccewir7pCVJkmYMg/QU2r5riKHhZE7Pnh0zz1m2gO27hrjr4S0tqEySJElTzSA9hbbsHATYo7UD4OhDZgPw4GM7prUmSZIkNYdBegpt6R8AGLW144iDZwHw4EaDtCRJ0kxgkJ5CW/rHHpFe0NdNT63CQwZpSZKkGcEgPYW29A/SVQ16anse1ojgiINn8dDG/hZUJkmSpKlmkJ5CW/oHmNPTRUSMuv7wg2fZ2iFJkjRDGKSnUDH13djXuClGpA3SkiRJM4FBegqNdjGWRocfPIt1W3ayc3BoGquSJElSMxikp8jg0DDbdw2NOmNH3eEH9wKwdpN90pIkSZ3OID1FtpZzSB+0j9YOcAo8SZKkmcAgPUV2T303ylUN6w4vg7Qzd0iSJHU+g/QU2dsc0nWHla0dXt1QkiSp8xmkp0j9qoZ7a+3oqVVZNKfHmTskSZJmAIP0FNmyc5DZ3VVqlb0f0sMPnsVDmwzSkiRJnc4gPUX2NfVd3REH9/plQ0mSpBnAID1FtvQP7HXqu7rD5xUXZcnMaahKkiRJzWKQngKZOf4R6fmz6B8Y5tFtu6ahMkmSJDXLvpOf9ql/cJjB4Rx16rs77riDV7zmDbvvb5p9BCz5Lc7/g3cze9dje2y/cP5crvjEZU2tV5IkSZNnkJ4C9Rk7RmvtGBiGMy943+776zb3c+8t97P8Zb/P8YsP2mP7b3/y/c0rVJIkSVPG1o4pMJ45pOvqYbseviVJktSZDNJTYH+CdG9XhVol2FJeUlySJEmdySA9BbbtHKQSMKurus9tI4I5vTW27DBIS5IkdbKmB+mIeHpEfCsibo+It0bEsRFxa0TcHREXN2z33nLZLRGxrNl1TaX+gSF6u6pExLi2n9PbxZadtnZIkiR1sqZ+2TAiuoDPA6/LzB+Wy64APgh8BbgpIr4MbAPOA5YDZwOXAuc3s7aptGNgaFyj0XVzemts2LCziRVJkiSp2Zo9Iv0C4NZ6iC6dAVyXmUPAteX9M4GV5bKVwGlNrmtK9Q8M07ufQXr7riEGh4abWJUkSZKaqdlB+mnAUER8t2zZ+C2gLzP7y/XrgCXAYmADQGYOAtWIGH8ybbGitWP8h3JuOXPHVr9wKEmS1LGaPY/0QcBhwEuAY4AvASOvjR1jLNtDRFwIXAiwdOnSKS10MnYMDHFYV++4tz+ovHDLlv5BDp7d3ayyJEmS1ETNHpHeCFyfmf2Z+XNgHrA1IuqpczHFqPQaYCFARNSALNs8niAzL8/MkzPz5EWLFjW59PHJzN1fNhyvvjJIb9+1x0uUJElSh2h2kP4a8PKIqEXEccCjwDeAc8rWjXOBG8qfs8plZwM3NrmuKbNraJjhHN/Ud3V93cW223bZ2iFJktSpmtrakZm/iIgvAj8EhoCLgJ8D1wAfAq7IzNUAEXElcCfFDB7nNrOuqdQ/UHxhcH9GpLtrFaqVYPtOR6QlSZI6VbN7pMnMvwP+bsTiU0fZbgWwotn1TLUdA0UY7u0e/+B+RDC7u+qItCRJUgfzyoaT1F/2Oe9PawdAX3fNHmlJkqQOZpCepP76iPR+BmlHpCVJkjqbQXqS6q0d+zsiPbunao+0JElSBzNIT1L/wDAB9NT271D2ddfYMTDE0PDIKbQlSZLUCQzSk7SjnEM6YtRryIypr7v4nucO+6QlSZI6kkF6kvb38uB1s3ucS1qSJKmTGaQnacd+XtWwrj4i7cwdkiRJnckgPUn9A0P7/UVDKGbtAEekJUmSOpVBepL6B4YnNCJdD9LO3CFJktSZDNKTkJnsmOCIdK1aoadWYbsj0pIkSR3JID0Jg8PJ0HBO6MuGUPRJb3NEWpIkqSMZpCehPnVdb/f+j0hDMXOHPdKSJEmdySA9Cf0TvKph3ezuqrN2SJIkdSiD9CTULw/eW5tYkO7rrtkjLUmS1KEM0pPQPzAMwKxJtHYMDCW7BoensixJkiRNA4P0JNRbOybzZUPAUWlJkqQOZJCehMm2duy+KIszd0iSJHUcg/Qk9A8M0VOrUKnEhB7f1+OItCRJUqcySE/CjoGhCV3VsO7xy4Q7Ii1JktRpDNKT0D8wPOGp76CYNi/CEWlJkqROZJCehP6BoQl/0RAgIpjdXbVHWpIkqQMZpCdhsq0d4FzSkiRJncogPQn9A0OTau0Ar24oSZLUqQzSEzQ4NMzAUE5+RLqnxradjkhLkiR1mqYG6YjoiohtEXF7+XNeRBwbEbdGxN0RcXHDtu8tl90SEcuaWddU2H1Vw6kYkR4YYjhzKsqSJEnSNKk1ef+HADdn5gvrCyLiCuCDwFeAmyLiy8A24DxgOXA2cClwfpNrm5Qdk7yqYV1fd43Mok1kdnez3w5JkiRNlWa3diwANoxYdgZwXWYOAdeW988EVpbLVgKnNbmuSXv88uCTH5EGr24oSZLUaZodpLuB50fEjyLiaxFxLNCXmf3l+nXAEmAxZeDOzEGgGhF7JNSIuDAiVkXEqvXr1ze59L2rB+lZ3ZMM0l7dUJIkqSM1NUhn5u3A4Zl5EnAVsAIY2QwcYywbbX+XZ+bJmXnyokWLprrc/bJjikak+8og7swdkiRJnaXps3Zk7v4W3eeBpwBbIqK3XLaYYlR6DbAQICJq5cPaOlnWv2w42R7pel+0M3dIkiR1lmbP2rGooUXjxcDtwPXAOeXyc4Ebyp+zymVnAzc2s66psGNgiK5qUKtM7hB21yp0VYNtjkhLkiR1lGZPE/EM4KMRsRN4DLgA2AJcA3wIuCIzVwNExJXAnRQzeJzb5LombefAED21ybV11M326oaSJEkdp6lBOjNvAJ46yqpTR9l2BUUPdUfoHxyedFtHXV93le3O2iFJktRRvLLhBO0cGKJ3qkake2psc0RakiSpoxikJ6h/cJieqRyRtkdakiSpoxikJ2hKe6R7auwcHGZwaHhK9idJkqTmM0hP0FT3SINzSUuSJHUSg/QEDA4NMzSc9EzyYix1u+eStk9akiSpYxikJ2DnYHkxlpoj0pIkSQcqg/QE9JeXB5/KHmnw6oaSJEmdxCA9Af2DU3N58LrZXY5IS5IkdRqD9ATsrI9IT1GPdKUSzOqq2iMtSZLUQQzSEzDVPdIAfT1e3VCSJKmTGKQnoH+KR6ShmLnDEWlJkqTOMa4gHRF/OcqyD0x9OZ2h3iPdM5Uj0l7dUJIkqaPU9rYyIo4HlgO/GxHfb1g1B/hd4D1NrK1t7RwYortWoRIxZfuc3VNj+84hcsr2KEmSpGbaa5CmGLF+PjAPeHXD8m3AW5pVVLvbOTg8pf3RALO7qwxlMlTpmtL9SpIkqTn2GqQz8y7gzyLi6sz8wTTV1Pb6B4amtD8aoK+8uuFgddaU7leSJEnNsa8R6bpKRHwOWATs7mfIzBc2pao2t3NweEr7o6GYtQMM0pIkSZ1ivEH6CuBPgF82sZaO0T8wxCF9PVO6z9nliPRAtXdK9ytJkqTmGG+Q3pqZX21qJR1k5+DwlF3VsK6vuxyRrhmkJUmSOsF4g/RNEXEN8C1gZ31hZl7WlKraWNKcHunuWoVqJWztkCRJ6hDjDdKPlD9zm1hLR8ioMpxTO4c0QEQwu7tqa4ckSVKHGFeQzsz/2exCOsVgpRuA3trUjkhDMXPHVoO0JElSRxhXkI6Ib8Me1wrJzHzR1JfU3obqQXqKe6ShmLljY83WDkmSpE4w3taOdzTcngW8iOLqhgecoWoRpKe6RxqKmTts7ZAkSeoM4xpWzcyfNvysysxLgZeP57ERsSgiNkTECyLi2Ii4NSLujoiLG7Z5b7nslohYNrGXMj3qI9JT3SMN5dUNq70MDA1P+b4lSZI0tcbb2nHRiEXHAQPjfI4PAT8tb78H+CDwFYqZQL5Mcbnx84DlwNnApcD549z3tHu8taM5PdIAj2zdxZJ5jkxLkiS1s/EOqy5q+FlIcWGWl+3rQRFxFrAW+HW56AzguswcAq4t758JrCyXrQRO258XMN3qrR29zRiRLq9uuH7Lzn1sKUmSpFbbr1k7ImJJeX/tvh4TEX3AnwLnAPX5pvsys7+8vQ44nGJEekO538GIqEZEtQzWbac+It3dhCBdH5Fev7UfmDfl+5ckSdLUGW9rx6nAPwFbi7sxG7gwM2/ay8M+APx1Zm6PiPqykTN/xBjLxqrjQuBCgKVLl46n9Ck3VOmmp1ah4TVNmfqI9MObHZGWJElqd+OdteOjwEsz8wGAiDgc+DJwyl4e8zvAeWXgXAicBWyOiN5yVHoxxaj0RuDJ5X5rFNPqjToanZmXA5cDnHzyySMD+LQYqnY3pT8ayhHpTNZu6t/3xpIkSWqp8fYndNdDdGkN0LO3B2TmMZm5LDOXAV8EXgtcD5wTEVXgXOCG8uesctnZwI379xKmV31EuhmqlaA21G+QliRJ6gDjHZH+UkR8A/gGRevFfwKumsDzXQJcQzGTxxWZuRogIq4E7qTolz53AvudNkOV5o1IA3QNbmftZoO0JElSu9trkI6ItwOfzsy/jIhTgFMpeprfAxw93ifJzDc13D11lPUrgBXj3V8rDTZxRBqga2iHI9KSJEkdYF8j0u/IzL8HyMybgZvrKyLiCuALTaytLQ1Vu+lpwuXB6xyRliRJ6gz7SoR762HYa4/0TJSZRWtHrYmtHUM72LRjgB272nL2P0mSJJX2FaRvi4i3jFwYEecB9zenpPa1bdcQRKXpPdKAo9KSJEltbl+tHW8DPh8R/xW4FRgCng70Ukxvd0DZtKO4KnpTe6QHdwCwdlM/xyzsa9rzSJIkaXL2GqQzcxPw0og4EVhOMWPHxzPzx9NRXLvZuH0XQFN7pGtD9RHpHU17DkmSJE3eeC8RvhpY3eRa2l59RLqpPdK7R6S9uqEkSVI7a97Q6gy0uR6km9gjXc1B5vTWWLvJEWlJkqR2ZpDeD9PRIw2wZG6vXzaUJElqcwbp/bBxexmkm9gjDbBkXi9rN9vaIUmS1M4M0vth044ByGG6q9MwIm1rhyRJUlszSO+HTTsGqA7vIiKa+jyHzetl/ZadDA4NN/V5JEmSNHEG6f1w3KKDmLv9oaY/z6HzehlOWL/V9g5JkqR2ZZDeD28+/RiOWn9z05/nsHm9QHFRFkmSJLUng3QbOnSuQVqSJKndGaTb0GHzZgE4BZ4kSVIbM0i3ofmzu+iuVQzSkiRJbcwg3YYigkPn9tjaIUmS1MYM0m3qsLmzDNKSJEltzCDdpg6d52XCJUmS2plBuk0dNq+XtZv6ycxWlyJJkqRRGKTb1KFze9k5OMzG7QOtLkWSJEmjMEi3qd0XZbG9Q5IkqS0ZpNvU7ouyGKQlSZLakkG6TdVHpNdsNEhLkiS1o6YG6Yg4PSJujogfRcT3I+LEiDg2Im6NiLsj4uKGbd9bLrslIpY1s65OcOjcXmqV4IHHtre6FEmSJI2i1uT9/xI4JzM3RMTvA+8CqsAHga8AN0XEl4FtwHnAcuBs4FLg/CbX1taqleDI+bO471GDtCRJUjtq6oh0Zq4pQ3QATwZ+CpwBXJeZQ8C15f0zgZXlspXAac2sq1MctWA29xukJUmS2lLTe6Qj4g+Bh4CTgI8DfZlZb/xdBywBFgMbADJzEKhGRLXZtbW7pQtmc69BWpIkqS01PUhn5seAw4GvA5cBI68wEmMs20NEXBgRqyJi1fr166e81nazdMFsNm4fYNMO55KWJElqN9Mya0cWl+f7DHAqsCUiestViylGpdcACwEiolY+ZGiU/VyemSdn5smLFi2ajtJb6uhDZgPY3iFJktSGmj1rxwkRUX+OlwI/Aa4HzilbN84Fbih/ziqXnQ3c2My6OsVRCwzSkiRJ7arZs3acBVwUETuAtcCFQD9wDfAh4IrMXA0QEVcCd1LM4HFuk+vqCPUg7cwdkiRJ7aepQbrsj/7YKKtOHWXbFcCKZtbTaeb2djF/dpdfOJQkSWpDXtmwzS11CjxJkqS2ZJBuc0ctmG1rhyRJUhsySLe5ow+ZzYOP7WBwaLjVpUiSJKmBQbrNLV0wm8HhZM2m/n1vLEmSpGljkG5zztwhSZLUngzSbW6pQVqSJKktGaTb3GHzZtFVDYO0JElSmzFIt7lqJThyvjN3SJIktRuDdAc4asFs7nvEIC1JktRODNIdYOmCWY5IS5IktRmDdAdYumA2m3YMsGn7QKtLkSRJUskg3QHqM3fc/5ij0pIkSe3CIN0BnEtakiSp/RikO0B9RPqeR7a1uBJJkiTVGaQ7wJzeLg6b18tda7e0uhRJkiSVDNIdYvmSOaw2SEuSJLUNg3SHWL5kDr9cv5WBoeFWlyJJkiQM0h3jxCVzGBhKfr3BPmlJkqR2YJDuEMsPnQtge4ckSVKbMEh3iOMW91GrBKvXbG51KZIkScIg3TF6alWOXdTHnY5IS5IktQWDdAdZvmSurR2SJEltwiDdQU5cMocHN+5gS/9Aq0uRJEk64BmkO8jyQ+cAcNfDjkpLkiS1WlODdEQcGRHXRcTPIuK7EXF4RBwbEbdGxN0RcXHDtu8tl90SEcuaWVenWr6kCNK2d0iSJLVes0ekdwJ/lZlPAb4AvBN4D/BBYDnwioh4SkQcDZxXLns/cGmT6+pIR86fxUE9Nb9wKEmS1AaaGqQzc31mfre8ew8wHzgDuC4zh4Bry/tnAivLZSuB05pZV6eKiOJS4WsM0pIkSa02nT3Srwa+DvRlZn+5bB2wBFgMbADIzEGgGhHVkTuIiAsjYlVErFq/fv00ld1eli+Zw+q1m8nMVpciSZJ0QJuWIB0RZwNLgS8CIxNgjLFsD5l5eWaenJknL1q0aOoL7QAnLpnD5v5B1m7u3/fGkiRJapqmB+mIOB74K+B3sxhG3RIRveXqxRSj0muAheX2NSDLNg+NUJ+5wy8cSpIktVatmTuPiDnAVcDvZeaacvH1wDkRcTVwLvBGYCPwzoh4H3A2cGMz62pnd9xxB694zRvGXD9Y6YJlr+J//M0nWbzp5wAsnD+XKz5x2XSVKEmSJJocpIF3AMcDV0YEwHbgt4FrgA8BV2TmaoCIuBK4E9hGEbAPSAPDcOYF79vrNg/edA99Jz6PM096NQDf/uT7p6M0SZIkNWhqkM7M/w3871FWnTrKtiuAFc2sZ6Y4/OBefr1hG5lJ+QFFkiRJ08wrG3agIw6eRf/AMI9u29XqUiRJkg5YBukOdMTBswB4aKMzd0iSJLWKQboDzZvVRV93lQc37mh1KZIkSQcsg3QHigiOOHgWD27c4YVZJEmSWsQg3aEOP3gWW3cOsrl/sNWlSJIkHZAM0h3qiPlFn7TtHZIkSa1hkO5Qh/R101Or8JBBWpIkqSUM0h1qd5/0YwZpSZKkVjBId7DDD57Fxh0DDFR7W12KJEnSAccg3cHq80lv613U4kokSZIOPAbpDrZoTg9d1WDrrENbXYokSdIBxyDdwaqV4OgFfWyefSRDw84nLUmSNJ0M0h3uSYcexGBtFrfc82irS5EkSTqgGKQ73LJD+ojhQa778ZpWlyJJknRAMUh3uO5ahTk71rDyJ2tt75AkSZpGBukZ4OCt97F+y05uvfexVpciSZJ0wDBIzwBztj9ET61ie4ckSdI0MkjPANUc5AXLF7HyJ2sYtr1DkiRpWhikZ4izn34YD2/eya332d4hSZI0HQzSM8SLnnwo3bUK19zxUKtLkSRJOiAYpGeIg3pqnP20JXzptgfZ3D/Q6nIkSZJmPIP0DPKW049l685BPn/L/a0uRZIkacYzSM8gTz9yHqccs4Ar//0eBoeGW12OJEnSjNb0IB0RCyLixoi4pLx/bETcGhF3R8TFDdu9t1x2S0Qsa3ZdM9Vbn38sD27cwdd+urbVpUiSJM1oTQ3SEdEFrATualj8HuCDwHLgFRHxlIg4GjivXPZ+4NJm1jWTvejExSw7ZDafvPHXZDoVniRJUrM0NUhn5gBwLnBjw+IzgOsycwi4trx/JrCyXLYSOK2Zdc1klUrwltOP4Y77N3qlQ0mSpCZqemtHZq4bsagvM/vL2+uAJcBiYEO5/SBQjYhqs2ubqV757COZN6uLj3/nl60uRZIkacZqxZcNR/YbxBjL9hARF0bEqohYtX79+qYUNxPM7q7xtjOO5Zur1/Hvd29odTmSJEkzUiuC9JaI6C1vL6YYlV4DLASIiBqQZZvHE2Tm5Zl5cmaevGjRomkruBO9+XnHcNSCWbz/mp85g4ckSVITtCJIXw+cU7ZunAvcUP6cVS47myf2VGsCeruq/PnLnsydD2/hKueVliRJmnK1FjznJcA1wIeAKzJzNUBEXAncCWyjCNgapzvuuINXvOYNeyxPoO+wF3LJv+3kqo9cQm24uOLhwvlzueITl01zlZIkSTPLtATpzPxUw+31wKmjbLMCWDEd9cw0A8Nw5gXvG3Xd07fs5LP/cR/dp7+VM5YX7TDf/uT7p7M8SZKkGckrG85wi+b0cNKR87j9gY3c9+j2VpcjSZI0YxikDwCnH7+QBbO7+cZP17J912Cry5EkSZoRDNIHgK5qhbOetoT+gWFu+Pm6PeYalCRJ0v4zSB8gFs3p4fQnLeTXG7bxyNwntbocSZKkjmeQPoA848h5HLOwj4cOeRbfWv1wq8uRJEnqaAbpA0hE8NKnHsqsXRu56F9u49Z7H211SZIkSR3LIH2A6alVOWbNd1gyt5c3f2oVdz28pdUlSZIkdSSD9AGoNryTT7/lFHpqFV73yZu5/f6NrS5JkiSp4xikD1BHLZjNZy84hVndFc7/xPf5fz9a0+qSJEmSOopB+gB2/OI5XH3R83jaEfN4+2dv46M3/IKhYSfHkyRJGg+D9AHukIN6+Je3nsLvPPNwPnzDXbzy4zfxC/umJUmS9skgLXq7qnz4/Gfy0dc+k3sf2cY5H/seK771C/oHhlpdmiRJUtuqtboATb877riDV7zmDaOuW1Lt5aFDns3ffGOYj173Q5Y8+iOO73qUKz9x2TRXKUmS1N4M0geggWE484L37XWb+x/dzvfu3sD9tdNYv3Mj/+9Ha3jZ05ZQqcQ0VSlJktTebO3QqI5aMJvXPucoXva0JWRUePtnb+MlH/kuX/7hAwwODbe6PEmSpJYzSGtMEcEJh87hhAeuY8XrfoNaJXjnv97BC//P/8fn/uM+dg0aqCVJ0oHL1g7t04/uuJ3LP/AndAFHzz6Cdf1P5c++tJ33fv4HLNx0Jws2/5JqDu7efuH8uVxhT7UkSZrhDNLap5E91ZnJfY9u55Z7HuPBWh+PHHoyTz9iHicdOY+5vV18+5Pvb2G1kiRJ08Mgrf0WERx9SB9HH9LH2k393HrfY9x2b/FzzMI+ctZhDA0nVb+YKEmSZjCDtCZlybxeznn6YWzeMcBPHtrETx7czI7DXsAp/+ubvPykwzj3GYfxG0fNd7YPSZI04xikNSXmzuriN49byCnHHMJXP/cpnvq0V/HZ/7iPT910Dwv6unn+kxZyxgmLeO4xCzji4FlEGKwlSVJnM0hrSlUrwcHb7ufjb3g2W/oH+NbqdXznzvV89671fOX2hwA4dG4Pz1o6v/g5+mCeevg8eruqLa5ckiRp/xikNeVGu3Li4cCC7vls613I9i0L+eYjC1n5k4MAiBziqUfO5ymHzeUph83lhEPncMyiPpbM7XXkWpIktS2DtKbceK6cCLBt5yBrN/dz83e/yfwTfosbfr6Oz696YPf6WV1Vjpg/i8Vzelg8p4d5s7ro7arS01Wlt6tCb61a3K9V6K5V6KpWdt+u3++uFrd7ahX6emrM7i62N6BLkqTJaqsgHRHvBX4PeAx4dWbe09qK1Ex9PTWOW3QQ9z16B59+yx+TmazbspNfrtvKrzZs41frt7Fm0w7WbdnJqnsfY0v/IP0DQ+yc5IVgKgF93TVm91SZ3V2E677uGrO6q/SVy/q6q8wqf88uA3h9u9kN29SqFaoRVCpQq1R2/57dXbVdRZKkGa5tgnREHA2cBywHzgYuBc5vaVGaFqO1goy0oPwBSODHP1vN2/760wwOJ0Oj/eQT76/857/jiKOPYThqDFeKn4Go8VilxoaoMVzpenxd1MhqF5WuXnZN4nLos7qqzJvVxcGzu3b/XtDXzSF9PRxyUDeHHNTDwr7i99xZtSeMsDvLiSRJ7a9tgjRwJrAyM4ciYiXw960uSNNjvK0gjW696JX7NeL71Xtu5dV/8ufj3v4j73glz3jGM0iC4UqNod0hu4vhSrX8XSOjQhKsXbOGQw8/AggyguFKF4OVbnZWe3io0s39lW4Gq90MVXoYrPZAVPb6/D21yu5Q3VWt8MgjG8ihIYJhyGEihwmy+J1JUC7L4d23Z3XX+E8veD5d1Qpdu1tdYvcoekQxJ3hQjNJHBJUAyt+V3esCGu9XIHj88ZUo7hf7qC/b97ZP3Gfxu75NV7Wot1YNuioVumpBrVKhqxp0VSu75yjPhCTJhOFMEsjhYtlwwtBwMjg8zOBQlh+6hhkczt33B4ca7w8zNJwMDOUTHjc0nAxnQnFoit8Ux6S4ze5WoWjcpnytjY+Dx4954zYRUKsEXbWiHal4/cVrrbcpdVXj8XW14vwZzuK1Zxavd9T7FPfLl/D4NuXyRo2vg4Yad7/mUrFX9thPlst2b1Wup2F5eToVx2HEeRZPOIf2PFcq9eUN52il/IvQyPXTbfcxKF9njly++359/ePn7dBwMjxc3s5keLg8f3ffLrZJeOKx4vFj1rhsj39fjdtX9rGPxnVTdBzr51/9fHz8PC1uD+eI1994f7jYdiiTzHzCew/Ffzue+N+gx8+rSsNrHu08q58z9X+3M6nlr/Hf9oh/5uRY2+2xj8bH5Jjr9nzu0R+3rzqGE4aHH/83MNS4rLw/p6fG4rm9Yz95C7RTkF4MbADIzMGIqEZENTOHWlyXDkD7G+4/dNEredUf/du4ts1MPvquN7L8pGczWO1hsNrLUKWbjCrDUWW4Ut19ezCqDESF/o2bOOHZz3/8Pywj/mcz8n88g5k8+NijfOZ7dxVhPypk2GqiA0clilmEGkM5PPFDQHGfJ9wYa/1YgXimq3/wGSt4N36Yrd8eLj/MPfGDVftrfK2PDyA8Htrrxhsw93jpEwyYY68b9WXMaIftvJ/vf/gPWl3GE8TIN6lVIuKPgcHM/HB5/yHgqMYgHREXAheWd5cDd057oROzkPJDgsbF4zV+Hqvx81iNn8dq/3i8xs9jNX4eq/3TzON1dGYuGm1FO41IrwGeDBARNSBHjkZn5uXA5S2obVIiYlVmntzqOjqFx2v8PFbj57EaP4/V/vF4jZ/Havw8VvunVcdr742a0+sG4KyIqFJ82fDGFtcjSZIkjaltRqQzc21EXEnRrrENOLfFJUmSJEljapsgDZCZK4AVra6jCTquHaXFPF7j57EaP4/V+Hms9o/Ha/w8VuPnsdo/LTlebfNlQ0mSJKmTtFOPtCRJktQxDNJNFhHvjYi7I+KWiFjW6nraWUR0RcS2iLi9/Dmv1TW1m4hYEBE3RsQl5f1jI+LW8hy7uMXltZ1RjtcHI+KX5fn19RaX1xYi4siIuC4ifhYR342Iwz2vxjbG8fK8GkVEnB4RN0fEjyLi+xFxoufW6MY4Vp5XexERiyJiQ0S8oJXnla0dTVRe9vzLwHMoZiJ5Q2Z62fMxRMQS4LOZ+cJW19KOIqIL+B7wE+D+zLwkIq4ArgW+AtwE/H5m/qyFZbaNMY7XP1CcY99tbXXtIyIWAU/OzO9GxH8DlgKH4Hk1qjGO1xw8r/YQEYcBA5m5ISJ+H/hNoIrn1h7GOFZDeF6NKSI+BRwD/AXwRlp0Xjki3Vy7L3sOrAROa3E97W4BTj4/pswcoJjNpnFqyDOA68pz7NryvhjzeHmOjZCZ6xv+R30PMB/PqzGNcbw8r0aRmWvKYBgU14n4KZ5boxrjWHlejSEizgLWAr8uF7XsvDJIN9cTLnsOVMt5sjW6buD55Z+2vhYRx7a6oHaTmetGLOrLzP7y9jpgyTSX1NZGOV5dwGci4icR8a5W1NTmXg18Hc+r8aofL8+rMUTEHwIPAScBH8dza0yjHCvPq1FERB/wp8D7Gxa37LwySDfXyL6ZaEkVHSIzbwcOz8yTgKuYmVMhTjXPsf3znzPzWcDpwOsj4rmtLqhdRMTZFG0KX8Tzap9GHC/PqzFk5seAwyk+cFyG59aYRjlWnlej+wDw15m5vWFZy84rg3RzraG49vuYlz3XE+XjTfufB57Sylo6xJaI6C1vL6b4JK4x1M+vzNwIXI/nGAARcTzwV8DvlsfI82ovRh4vz6u9K4/PZ4BT8dzaq8Zj5Xk1pt8BPh4R9wCvAj5HC88rg3Rzednz/VB+A7fe+vJi4PYWltMprgfOKY/buRTnnMZQfqGHiOgGng/c0dqKWi8i5lD8Bej3MnNNudjzagyjHS/Pq9FFxAkRUc8ZL6X44q/n1ihGO1aeV6PLzGMyc1lmLqP4i9BraeF51VZXNpxpvOz5fnsG8NGI2Ak8BlzQ4no6wSXANcCHgCsyc3Vry2l7l0XEcRR/Bvx0Zv6w1QW1gXcAxwNXFt9zYjvw23hejWW04/Ww59WozgIuiogdFF8MuxDox3NrNKMdK/97NX6X0KLzyunvJEmSpAmwtUOSJEmaAIO0JEmSNAEGaUmSJGkCDNKSJEnSBBikJUmSpAkwSEvSDBERB+9lXdc0liJJBwSDtCTNABFxNPD1iHh5RKyKiM0RUYuIr0XELOBrDdt+sfz97oj43oif32jVa5CkTuMFWSRpBsjMeyPiQuChzDw5Ihqv7PU24OqIeCHw18CxEbEKeD2wpGG7HV70QZLGzxFpSZoBIuKVwHOAjIjvASc3rD4Z+ASwAFgBfIvi6mnHAFXgU+XPmdNXsSR1PkekJWlm+H/AtcBDwE2ZeXpEvAN4KvB14DLgvvKnbifwIqDezjFn+sqVpM5nkJakGSAz+yPipcC5wIZy8b9TjDyfCHwAWEQxAr37YcDljfuJiGWZeU/TC5akGcDWDkmaOV4CLAVeERGPAXcBjwA3AuuBHQ3bVoDXAtuAU4EB4OnA06azYEnqZAZpSZo53gH8S2aeDtxM0boBxYwd5wPbKYLzQmBNue71FL3VrwWeN63VSlKHM0hL0gwQEccD1cx8JCK6gcFyVS9wNHAGsDozvwBsyMy3lOvfAXwBeCfFFw4lSeNkkJakmeHFwMcj4gTgJuBzwBDFCPSvgHdRjDyP9M/A6cBHgP86LZVK0gwRmdnqGiRJUyAiIv2PuiRNG4O0JEmSNAG2dkiSJEkTYJCWJEmSJsAgLUmSJE2AQVqSJEmaAIO0JEmSNAEGaUmSJGkCDNKSJEnSBPz/532AfGnTqbwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\u001b[1;31m2009-10-19\u001b[0m收盘到\u001b[1;92m2022-10-20\u001b[0m收盘A股涨跌幅#########################\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>涨幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1538.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.041460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.913208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.889922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.190770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.247446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.140823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49.718236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                涨幅\n",
       "count  1538.000000\n",
       "mean      1.041460\n",
       "std       2.913208\n",
       "min      -0.889922\n",
       "25%      -0.190770\n",
       "50%       0.247446\n",
       "75%       1.140823\n",
       "max      49.718236"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\u001b[1;31m2008-10-19\u001b[0m收盘到\u001b[1;92m2022-10-20\u001b[0m收盘A股涨跌幅#########################\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>涨幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1508.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.770660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.398663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.724671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.708660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.812480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.118526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>111.825739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                涨幅\n",
       "count  1508.000000\n",
       "mean      3.770660\n",
       "std       7.398663\n",
       "min      -0.724671\n",
       "25%       0.708660\n",
       "50%       1.812480\n",
       "75%       4.118526\n",
       "max     111.825739"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\u001b[1;31m2007-10-19\u001b[0m收盘到\u001b[1;92m2022-10-20\u001b[0m收盘A股涨跌幅#########################\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>涨幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1401.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.868847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.205812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.926936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.370249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.035517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.982928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>54.230901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                涨幅\n",
       "count  1401.000000\n",
       "mean      0.868847\n",
       "std       3.205812\n",
       "min      -0.926936\n",
       "25%      -0.370249\n",
       "50%       0.035517\n",
       "75%       0.982928\n",
       "max      54.230901"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mzh1uW8sG6UWLFrFs2bJGlyFJkqRJLCLuG2mbUzskSZKkGhikJUmSpBoYpCVJkqQaGKQlSZKkGtQ9SEfEpyPiVxGxLCKWRMTiiLghIu6KiLOq9ju7bPtFRCyqd12SJEnS7qjrqh0R8VzgGZl5REQcB3wEWAmcC3wH+GlEXApsAk4FDgFOBM4DXlfP2iRJkqTdUe8R6Y3A1IgIYCawFjgOuDIzB4ArysfHA1eVbVcBx9a5LkmSJGm31HVEOjNvjYhrgJ8DSTHq/MvM3FrusgpYQDEivaZ8Tn9EtEdEexmsJUmSpKZT1xHpiJgDvAL4NLAZeBlFoH7CbiO0DXe808u51stWr1493uVKkiRJo1bvqR1vBK7IzH8H/pBibvSGiOgpt8+jGJVeAcwBiIgOIIcbjc7MCzJzSWYumTt32Cs1SpIkSROi3kF6MzC/vL83sAW4BjgpItqBk4Fry9sJZduJwHV1rkuSJEnaLXWdIw18lSIg30wxfeMM4CbgcuATwIWZuRwgIi4CbqeYL31yneuq2Z++8wzWrF0/6v3nzJ7BhZ8/v44VSZIkqRHqfbLhVuA1w2w6Zph9lwJL61nPeFizdj3Hv+NDo97/h1/4SB2rkSRJUqN4ZUNJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGdQ3SEXFWRNxUddsaEYsj4oaIuCsizqra9+yy7RcRsaiedUmSJEm7q65BOjPPzcyjMvMo4E3ANcAHgXOBQ4BTIuLpEXEAcGrZ9hHgvHrWJUmSJO2uiZza8Tbgy8BxwJWZOQBcUT4+HriqbLsKOHYC65IkSZLGbEKCdER0AicB3wWmZubWctMqYD4wD1gDkJn9QHtEtA9znNMjYllELFu9evVElC5JkiQNa6JGpE8GfpCZ24Acsi1GaHuSzLwgM5dk5pK5c+fWoUxJkiRpdCYqSP8p8G/l/Q0R0VPen0cxKr0CmAMQER1AltM8JEmSpKZU9yAdEfsCB2bmz8uma4CTyqkbJwPXlrcTyrYTgevqXZckSZK0Ozom4DX+GPha1eMPA5cDnwAuzMzlABFxEXA7sIkiYEuSJElNq+5BOjPPG/J4NXDMMPstBZbWux5JkiRpPHhlQ0mSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBgZpSZIkqQYGaUmSJKkGBmlJkiSpBnUP0hFxeET8ICJuioi3R8TiiLghIu6KiLOq9ju7bPtFRCyqd12SJEnS7uio58EjohP4BvDGzPxl2XYhcC7wHeCnEXEpsAk4FTgEOBE4D3hdPWuTJEmSdke9R6RfBNxQCdGl44ArM3MAuKJ8fDxwVdl2FXBsneuSJEmSdku9g/QzgYGI+HE5ZeP3gamZubXcvgqYD8wD1gBkZj/QHhHtda5NkiRJqlldp3YA04B9gZcBBwLfBnLIPjFC25NExOnA6QD777//uBYqSZIkjUW9R6TXAddk5tbMvA2YCWyMiJ5y+zyKUekVwByAiOgAspzm8QSZeUFmLsnMJXPnzq1z6ZIkSdLI6h2krwZeGREdEXEQ8CjwfeCkcurGycC15e2Esu1E4Lo61yVJkiTtlrpO7cjMOyPim8AvgQHgDOA24HLgE8CFmbkcICIuAm6nWMHj5HrWJUmSJO2ues+RJjP/BfiXIc3HDLPfUmBpveuRJEmSxoNXNpQkSZJqYJCWJEmSamCQliRJkmpgkJYkSZJqYJCWJEmSamCQliRJkmpgkJYkSZJqYJCWJEmSamCQliRJkmpgkJYkSZJqYJCWJEmSamCQliRJkmpgkJYkSZJqYJAeZxu29nHTA+vIzEaXIkmSpDrqaHQBk81P7lzDHas2ss+Mbvad2dvociRJklQnjkiPo8e29HHnqo0ALF+5ocHVSJIkqZ4M0uPoxvvX0hbBfrN6uePhDQwMOr1DkiRpsjJIj5PN2/u59aH1HLrvdJ51wCy29g1y36ObGl2WJEmS6sQ50uPkVw8+Rv9gcvT+s5nZ20lPZxu3r9hAT6MLkyRJUl04Ij0O+gYGufnBdSyeM5W9pnbR3hY8bd507l6ziYHwu4okSdJkZJAeB8tXbmBr3yDPPmD2jrZD953OwGDy2NSnNLAySZIk1YtBehysWr+V3s52Fsx6fLm7+TN6mNnbybppixpXmCRJkurGID0O1m3uY9aUzie0RQSH7DOdjb378MjGbQ2qTJIkSfVikB4H67b0Mau380ntC2b1QAR3PLyxAVVJkiSpnuoapCOiMyI2RcRN5e3UiFgcETdExF0RcVbVvmeXbb+IiEX1rGs89Q0MsnFbP7OmdD1p2+ypRds9awzSkiRJk029l5TYG7g+M19caYiIC4Fzge8AP42IS4FNwKnAIcCJwHnA6+pc27h4bEsfADOHGZGe3t1BDPZz9yrXk5YkSZps6j21Yy9gzZC244ArM3MAuKJ8fDxwVdl2FXBsnesaN+s2F0F66BxpKOZJd/dtcERakiRpEqp3kO4CXhgRv4qIqyNiMTA1M7eW21cB84F5lIE7M/uB9ohor3Nt42Ldlu3A8EEaoLtvPXevNkhLkiRNNnUN0pl5E7AgM48ALgaWAjlktxih7Uki4vSIWBYRy1avXj3e5dbksc199Ha2090xfO7v7lvPg2u3sLVvYIIrkyRJUj3VfdWOzKyE5G8ATwc2RETlytnzKEalVwBzACKio3zak5JnZl6QmUsyc8ncuXPrXfqoDLf0XbXu7RvIhHsfcZ60JEnSZFLvVTvmVk3ReClwE3ANcFLZfjJwbXk7oWw7EbiunnWNp5GWvqvo6VsPwD2rDdKSJEmTSb1X7TgS+FREbAPWAu8ANgCXA58ALszM5QARcRFwO8UKHifXua5xUVn6bubORqTLIH33KudJS5IkTSZ1DdKZeS3wjGE2HTPMvksp5lC3jMrSd7N6n7yGdEVbDrBgZg/3rHFEWpIkaTLxyoa7YWdL31VbPHca97hyhyRJ0qRikN4NO5a+28kcaYDFc6dy9+pNPH7epSRJklqdQXo37Fj6rnPnS14fNHcaG7f1s3rDtgmqTJIkSfVmkN4Nu1r6rmLx3KkA3OX0DkmSpEnDIL0b1m3pY+YupnVAMSINLoEnSZI0mRika1RZ+m40I9LzZ/TQ29nupcIlSZImEYN0jUaz9F1FW1tw4JypjkhLkiRNIgbpGo126buKg+ZN4541jkhLkiRNFgbpGo126buKxXOm8uDaLWztG6hnWZIkSZogBukard/ST3dH2y6XvqtYPHcqmXDfI5vrXJkkSZImgkG6Rpu39zOte/RXWH/KXlMAeHCtQVqSJGkyMEjXaNO2AaZ0j240GmC/Wb0A/G7dlnqVJEmSpAk0qiAdER8dpu2c8S+ndWza3s/UrtGPSM+Z1k1Xexu/W2uQliRJmgx2mgQj4mDgEOANEfGzqk3TgTcAH6xjbU0rM9m8bYCpY5ja0dYWLJjVw4OOSEuSJE0Ku0qCbcALgZnAaVXtm4C31auoZre1f5CBTKZ2jX5qB8DC2b2OSEuSJE0SOw3SmXkH8NcRcVlm/nyCamp6m7b1A4xpRBpg4axefnj76nqUJEmSpAk22iTYFhFfB+YCUWnMzBfXpaomVwnSU8Y6Ij1rCqs3bGNr3wA9o1w2T5IkSc1ptEH6QuAvgbvrWEvL2Ly9uKjKmEekZxcrd6x4bCsHzpk67nVJkiRp4ow2CW7MzO/WtZIWsml7ObVjDKt2QDG1A+B3a7cYpCVJklrcaJPgTyPicuAHwLZKY2aeX5eqmtymbQN0tgddHWNbhnu/2ZW1pL0oiyRJUqsbbZB+pLzNqGMtLWPztn6mjHE0GmD+zB7aAlfukCRJmgRGlQYz8+/qXUgr2bR9gKljuKphRWd7G/vMcC1pSZKkyWBUQToifgjkkObMzJeMf0nNb9O2fuZO767puQtnuZa0JEnSZDDa+QlnVt3vBV5CcXXDPdKm7f0s6q7tZMGFs3u54b6141yRJEmSJtqozpbLzFuqbssy8zzglaN5bkTMjYg1EfGiiFgcETdExF0RcVbVPmeXbb+IiEW1/SgTYyA66BsY+1UNKxbO6mXlY1sZGBw6wC9JkqRWMtqpHWcMaToI6Bvla3wCuKW8/0HgXOA7FCuBXEpxufFTgUOAE4HzgNeN8tgTrr+9Bxj7GtIVC2f30j+YPLx+KwvK5fAkSZLUeka7ftvcqtsciguzvGJXT4qIE4CVwG/LpuOAKzNzALiifHw8cFXZdhVw7Fh+gInW11GE37Fe1bBix1rSnnAoSZLU0sa0akdEzC8fr9zVcyJiKvBXwElAZb3pqZm5tby/ClhAMSK9pjxuf0S0R0R7Gaybzu6OSO9YS3rtFp6zaLyqkiRJ0kQb7dSOY4AvAhuLhzEFOD0zf7qTp50DfDwzN0dEpW3oxOAYoW2kOk4HTgfYf//9R1P6uOtvL4JwrUF6gSPSkiRJk8Jo0+CngJdn5oMAEbEAuBR43k6e8yrg1DJEzwFOANZHRE85Kj2PYlR6HXBYedwOimX1hh2NzswLgAsAlixZ0pCz9fo6emkL6BnjVQ0rpnR1sNfULh50CTxJkqSWNto02FUJ0aUVwE4XUs7MAzNzUWYuAr4JvB64BjgpItqBk4Fry9sJZduJwHVj+xEmVn97L1O6OqgaZR+zhbN6HZGWJElqcaMdkf52RHwf+D7F1Is/AC6u4fU+DFxOsZLHhZm5HCAiLgJup5gvfXINx50wfe09NV3VsNrCWb3cuWrDOFUkSZKkRthpkI6IdwNfycyPRsTzgGMo5jR/EDhgtC+SmW+tenjMMNuXAktHe7xG6u/oZWpXbfOjKxbO7uVHd6wiM3drZFuSJEmNs6tEeGZmfgYgM68Hrq9siIgLgUvqWFtT6mvvrflEw4qFs3rZ2jfIo5u2s/e02i41LkmSpMba1Rzpnc1h2OMS4Pb+QQbau2u+qmHFwtmu3CFJktTqdhWkb4yItw1tjIhTgQfqU1LzWr1xG1D70ncVOy7K4sodkiRJLWtXifCdwDci4s+AG4AB4HCgh2J5uz3K6g1FkJ6ymycb7ueItCRJUsvbaZDOzMeAl0fEocAhFCt2fDYzfz0RxTWbVeuLizLu7smGM3s7mdrV7lrSkiRJLWy0lwhfDiyvcy1Nb9WG8ZnaEREsnO1a0pIkSa2stsvz7aFWbdgGmUzp3L2pHVBelMURaUmSpJZlkB6D1Ru20jGwlba23V/72RFpSZKk1maQHoMTnrkv+6z9zbgca+GsKTy2pY+N2/rH5XiSJEmaWAbpMTjuaXPZe8Nd43KsHWtJO71DkiSpJRmkG2THWtLrNje4EkmSJNXCIN0g+zkiLUmS1NIM0g0yd1o3Xe1tPOgJh5IkSS3JIN0gbW3BvrN6HJGWJElqUQbpBlo4yyXwJEmSWpVBuoG8KIskSVLrMkg30MLZvazasI1t/QONLkWSJEljZJBuoMoSeCvWbW1wJZIkSRorg3QD7bgoi/OkJUmSWo5BuoH2mzUFcC1pSZKkVmSQbqD5M3uIwLWkJUmSWpBBuoG6OtrYZ7prSUuSJLUig3SDLZzdy+/WbW50GZIkSRqjjkYXMNndfPPNnPLaN4+4/f55x7K5ew6nvPYzAMyZPYMLP3/+RJUnSZKkGtU1SEfEC4B/BHqBTcCfANuBS4CZwEWZeW6579nAW4C1wGmZeW89a5sofYNw/Ds+NOL2/75rDTfev5bj3n42bRH88AsfmcDqJEmSVKt6j0jfDZyUmWsi4k+A9wHtwLnAd4CfRsSlFCH7VOAQ4ETgPOB1da6tKUzv6WAwYdO2fqb3dDa6HEmSJI1SXedIZ+aKMkQHcBhwC3AccGVmDgBXlI+PB64q264Cjq1nXc1kRhmeN2ztb3AlkiRJGou6n2wYEe8FHgKOAD4LTM3MyqX8VgHzgXnAGoDM7AfaI6J9mGOdHhHLImLZ6tWr6136hJjRWwTpx7b0NbgSSZIkjUXdg3RmfhpYAHwPOB/IIbvECG3DHeuCzFySmUvmzp077rU2wozeDgJYZ5CWJElqKROy/F1mJvBV4BhgQ0T0lJvmUYxKrwDmAERER/mUgYmordE62tqY3tPBY5sN0pIkSa2krkE6Ip4WEZXXeDnwG+Aa4KRy6sbJwLXl7YSy7UTgunrW1WxmTulk3ZbtjS5DkiRJY1DvVTtOAM6IiC3ASuB0YCtwOfAJ4MLMXA4QERcBt1Os4HFynetqKrN6u7jz4Q2NLkOSJEljUNcgXc6P/vQwm44ZZt+lwNJ61tOsZvV2srV/kK19e8RsFkmSpEnBS4Q3gZlTipU7POFQkiSpdRikm8CsyhJ4nnAoSZLUMgzSTWBmb2VE2hMOJUmSWoVBugl0tLcxrdsl8CRJklqJQbpJzOrtdI60JElSCzFIN4mZUzpZ54i0JElSyzBIN4lZvZ1s6RtgIOq9tLckSZLGg0G6SVSWwNveOb3BlUiSJGk0DNJNYlZvFwDbOqc1uBJJkiSNhkG6ScxyRFqSJKmlGKSbRGd7G1O72tnW4Yi0JElSKzBIN5GZUzodkZYkSWoRBukmMqu3yznSkiRJLcIg3URmTumkv2MKm7f3N7oUSZIk7YJBuonM6i1OOLzvkc0NrkSSJEm7YpBuIpUgfe+aTQ2uRJIkSbtikG4is6d2QSZ3PLyx0aVIkiRpFwzSTaSzvY2uvg0sX7m+0aVIkiRpFwzSTaZ3+zqWr9zQ6DIkSZK0CwbpJtOzfR33PrLJlTskSZKanEG6yfRsX0cmzpOWJElqcgbpJtOzfR0Ay1c4T1qSJKmZGaSbTFf/JqZ2tTtPWpIkqcnVNUhHxH4RcWVE3BoRP46IBRGxOCJuiIi7IuKsqn3PLtt+ERGL6llXMwvgkPnTuc0RaUmSpKZW7xHpbcDHMvPpwCXAnwMfBM4FDgFOiYinR8QBwKll20eA8+pcV1M7dN8ZLF+5gcxsdCmSJEkaQV2DdGauzswflw/vBWYDxwFXZuYAcEX5+HjgqrLtKuDYetbV7A6bP53HtvSxcv3WRpciSZKkEUzkHOnTgO8BUzOzkhBXAfOBecAagMzsB9ojon0Ca2sqh+47A4DlK5wnLUmS1KwmJEhHxInA/sA3gaHzFWKEtuGOc3pELIuIZatXrx7/QpvEIfOnA3CbVziUJElqWnUP0hFxMPAx4A1ZTPrdEBE95eZ5FKPSK4A55f4dQJbTPJ4gMy/IzCWZuWTu3Ln1Lr1hZvR0snBWryPSkiRJTazeq3ZMBy4G3pKZK8rma4CTyqkbJwPXlrcTyrYTgevqWVcrOGzf6Sx3RFqSJKlpddT5+GcCBwMXRQTAZuAPgcuBTwAXZuZygIi4CLgd2EQRsPdoh86fwQ9vX83WvgF6OvfY6eKSJElNq65BOjP/AfiHYTYdM8y+S4Gl9aynlRy673QGBpO7Vm3kmQtnNrocSZIkDeGVDZvUofOLlTu8MIskSVJzMkg3qQPnTGVGTwc33r+20aVIkiRpGAbpJtXeFixZtBfX//bRRpciSZKkYRikm9hzD9yLe1ZvYvWGbY0uRZIkSUMYpJvYcw/cC4Bl9zoqLUmS1GwM0k3smQtm0tvZ7vQOSZKkJmSQbmJdHW0cfcAs/scgLUmS1HQM0k3uuYv25raV63lsS1+jS5EkSVIVg3STe+6Be5EJN9znqLQkSVIzMUg3uWftP4vO9nCetCRJUpMxSDe5ns52jtjPedKSJEnNxiDdAp574F78+sHH2Ly9v9GlSJIkqWSQbgHPPXAv+geTm+5f1+hSJEmSVDJIt4BnHzCb9rbgurvWNLoUSZIklQzSLWBGTyfHLt6bq369gsxsdDmSJEnCIN0yTjx8X+59ZDO3rdjQ6FIkSZKEQbplvPwZ+9AWcOWvVzS6FEmSJGGQbhl7T+vmmMV7c6XTOyRJkpqCQbqFnHj4vtyzZhO3P+z0DkmSpEYzSLeQE545v5je8Sund0iSJDVaR6ML0BPdfPPNnPLaN4+4vXffF/P5qx/jh//6UaJsmzN7Bhd+/vyJKVCSJEmAQbrp9A3C8e/40Ijb935wHT+8fTVHvuGv2HtaNwA//MJHJqo8SZIklZza0WIOmjuNAOdJS5IkNZhBusVM7e7gwDlT+fXvHqNvYLDR5UiSJO2x6h6kI2KviLguIj5cPl4cETdExF0RcVbVfmeXbb+IiEX1rquVHb3/bLb2DXLbivWNLkWSJGmPVdcgHRGdwFXAHVXNHwTOBQ4BTomIp0fEAcCpZdtHgPPqWVerWzCrh31mdPPL+9cx6JrSkiRJDVHXIJ2ZfcDJwHVVzccBV2bmAHBF+fh44Kqy7Srg2HrW1eoigqP3n826LX38ds2mRpcjSZK0R6r71I7MXDWkaWpmbi3vrwLmA/OANeX+/UB7RLTXu7ZWdvDcaczo6eCG+9Y2uhRJkqQ9UiNONhw6FyFGaHuSiDg9IpZFxLLVq1fXpbhW0dYWHPWUWax4bCubuvdudDmSJEl7nEYE6Q0R0VPen0cxKr0CmAMQER1AltM8niAzL8jMJZm5ZO7cuRNWcLN6xoKZdHe08fDsw0nnSkuSJE2oRgTpa4CTyqkbJwPXlrcTyrYTeeKcao2gq6ONYxbvzcYp+3L1b1Y2uhxJkqQ9SiOC9IeBDwB3At/NzOWZuRK4CLgd+Cjwlw2oqyUdsXAmPdvW8pErbmXTtv5GlyNJkrTHmJBLhGfml6rurwaOGWafpcDSiahnMmlrCxauWcbd3X/Ap39wJ3/zisMaXZIkSdIewSsbTgJTt63htGfvxxev+y13rfLS4ZIkSRPBID1J/PUrDmVqdwfvu+RXbOt/0nmakiRJGmcG6Uli72ndnPfqw7n5gXWcc8VtjS5HkiRp0jNITyInPHNf3vn7i/nKz+/j0l8+2OhyJEmSJjWD9CTzgZcfwjGL9+Jvvv1rbluxvtHlSJIkTVoG6Ummo72Nf3nD0czs7eRPv/QLHnh0c6NLkiRJmpQmZPk71dfNN9/MKa998xPaZnTN4p59X8xLzr2cgx66ls6BLTu2zZk9gws/f/5ElylJkjSpGKQngb5BOP4dH3pS+8r1W7n0xt+x8rDTePXR+zG1u+juH37hIxNdoiRJ0qTj1I5JbP6MHk45agEbtvbzrRsfZP2WvkaXJEmSNGkYpCe5hbN6edVRC9m8fYD/WPYAqzZsbXRJkiRJk4JBeg+wcHYvpz17P9oi+OYND7K+d99GlyRJktTyDNJ7iL2ndfO65zyFWb1d3Dv/OD557R0MDGajy5IkSWpZBuk9yLTuDk5bsh+zNt7LJ6+9k7de9D+s2bit0WVJkiS1JIP0HqazvY2nrP45H/ujw7n+t4/yik9dx/duWdnosiRJklqOQXoPFMDrn7s/l53xfOZM6+adX7mBM792I484Oi1JkjRqBuk92NMXzOC7Zz6f9/3B0/jeLSt5yT/9P778s3vpHxhsdGmSJElNzyC9h+tsb+M9L3kq//neF3LY/Bl86Du38IpPXcePbl9FpicjSpIkjcQrG+6BhrukOEACB0xZyH19z+KtF21kytbV7LP21yzq2cJFXlJckiTpCQzSe6CRLile0T84yC0PrWfZvR38tmcuD29dzfduWclLD9uH9raYwEolSZKal0FaT9LR1saR+83iGfvO4JaH1vPft2zinV+5gQP2nsJbf28Rf/Ss/Zg5pbPRZUqSJDWUc6Q1oo72No58yiwOfeBylr7xWew1tYu/u/xWnvP31/Kei3/Jj+9YTZ8nJkqSpD2UI9LapSB55RELeOURC/jN7x7jkmUPcNlND3H5zQ8xo6eD4w+dx0sP24fnHbgX82b0NLpcSZKkCWGQ1pg8c+FMnrlwJn9z4mH8vztWc82tD/OD5av4zk0PAbBwVi/P2n8WB8+bxoFzprL/XlOYM62bGb2dTO/uoM051pIkaZIwSGuXRlrlo2IhwV7de7Gpew4bN87h2kfm8J8dUxi6el4ETO/uYOaUTmb2djKjp5PpPR3ln53M6O1gek8ne03tZM60bvae2s2c6V3sPbXbkxwlSVLTaaogHRFnA28B1gKnZea9ja1IsOtVPob65Jmv5vCjnsX2jmls75xGf1s3A+1dDLR1MdDWyYa2LtaV9wfbu9g6ELR1T2GwbYQTGDNpH9xG58BWOvq30jG4lekdg7zhVa9gzrRu5k7rZu9pXcyZ1s3sKV30dLYRYfCWJEn11TRBOiIOAE4FDgFOBM4DXtfQolSTvkF4yds/OOr9P3HGq/nA+d9iMJPt/YNs6Rtg87YBNm/vZ/P2gfLWX7SXj3+3aTMfv/r2YY8XAVM62+nt6mBKVztTutrprfzZ+cS23s522tuCtgja22LH/bbgSe2d7UF7W1v5Z9DRFnR3ttPTURyrp7Pt8fsd7fR0tdHVbqiXJGmyapogDRwPXJWZAxFxFfCZRhekidUWQU9nOz2d7cyesvN9i1Hvo+lr76G/6jbQ3s1gtDPY1sFgdLA+OljX1kF7VzcHHvRUHtm4fUcg37J9gK19AwxkPmkayniJoCpct9HZ0UZ7BCsfXsnAwACRCSSRSfD4/Sf8SdLV0c6zjjyC9rYhAT+Cth1/Ftu62tvp7Ai629vobG+jq6O4Ve53V+63j9Be7r/jmOVrVV6n8vodZVstsnzPK2/7YCYDg/n4n4MwUN4fGEwGMhkcer96+47n8oTjDGQyMPDk50RUfhZ2vJdDf77iCxRV+w7f3la+T5VtEVS1P/FLWUSxX1A8P8rPSCt82apc6bT6/5Ucsu2JbdX7Pfm5DNkvyWHaimMPFv8rMJhZ3srPEOx4PDj4xP+PK29p5f2N6jaKvojiwY7H5cMd+1dev/I6ZKWtqLfyGR4czB2v1RaPfxmP8s/qz0Jl++P7suNxK3wOtPuy6jM8WH6uc8jne7jP+459KT5zrfB5G/r3Rla3Uf6Mg+W/AeXf04NZ/D2e+fjf2Vm2Te3uYO707rrXPRbNFKTnAWsAMrM/Itojoj0zBxpcl5pQMep91qj3/+SZr+YpRx5JF9AFzByyvfxnkCQggt/cehvv+vi/7fjL7fG/5Cr/4xf3+wcH6R9IvvWZczjgoKeS0V4E+egg2yr328noYEtbO5tpIyPYvH49Bx15zOP/EFeCZeW1Kv9Il/9gr7zvbn5y03KIKKN17Li/40+CjCCjjaSdbGsfp3d75yqBvpI8KgGj8r5WgohXnN+5yltY+ccwioRH8Pg/fBW7Cp9PvDN8kB1tCNbEqQ47T/gcjFL152FU+49h9zF/JMb4hLHUPtbP51hrzzG8wNiPPcYn1NFIn7ehf1/kjv88+e+SocG4els97LvtAX72z++q3wvUIMbygamniPgA0J+Z/1w+fgh4SnWQjojTgdPLh4cAw/9uvznMofxioJZj37Um+6012W+ty75rTfbb2B2QmXOH29BMI9IrgMMAIqIDyKGj0Zl5AXBBA2obs4hYlplLGl2Hxs6+a032W2uy31qXfdea7Lfx1UxXNrwWOCEi2ilONryuwfVIkiRJI2qaEenMXBkRF1FM19gEnNzgkiRJkqQRNU2QBsjMpcDSRtcxTlpiCoqGZd+1JvutNdlvrcu+a0322zhqmpMNJUmSpFbSTHOkJUmSpJZhkK6DiDg7Iu6KiF9ExKJG16Odi4i9IuK6iPhw+XhxRNxQ9uHoF6vWhImI/SLiyoi4NSJ+HBEL7LfmFxEviIjrI+JXEfGziDjUfmsdETE3ItZExIvst9YQEZ0RsSkibipvp9p348upHeOsvNT5pcBzKFYfeXNmeqnzJhURncBPgN8AD2TmhyPiQuAK4DvAT4E/ycxbG1imhoiIucBhmfnjiHgPsD+wN/ZbU4uIfYG+zFwTEX8C/B7Qjv3WEiLiS8CBwN8Cf4z91vQiYj7wtcx8cVWb/8aNI0ekx9+OS50DVwHHNrge7URm9lGsEFO93OJxwJVlH15RPlYTyczVmfnj8uG9wGzst6aXmSvKEB0U1w24BfutJUTECcBK4Ldlk/3WGvbiyRdfse/GkUF6/D3hUudAe7k2tppUZq4a0jQ1M7eW91cB8ye4JI3NacD3sN9aQkS8F3gIOAL4LPZb04uIqcBfAR+parbfWkMX8MJyOtXVEbEY+25cGaTH39C5MtGQKrQ77MMWEREnUkzr+Cb2W0vIzE8DCyi+/JyP/dYKzgE+npmbq9rstxaQmTcBCzLzCOBiiiWG7btxZJAefysormM/4qXO1fQ2RERPeX8exTd2NZmIOBj4GPCGLE72sN9aRNlfXwWOwX5rBa8CPhsR9wKvAb6O/dYy8vGT4b4BPB37blwZpMeflzpvfdcAJ5V9eDJFn6qJRMR0itGVt2TmirLZfmtyEfG0iKj8u/NyipN87bcml5kHZuaizFxE8duf12O/tYRypZXK9NKXAjdh342rprqy4WTgpc4nhQ8DlwOfAC7MzOWNLUfDOBM4GLioOG+NzcAfYr81uxOAMyJiC8WJa6cDW7HfWtGHsd9awZHApyJiG7AWeAewAftu3Lj8nSRJklQDp3ZIkiRJNTBIS5IkSTUwSEuSJEk1MEhLkiRJNTBIS5IkSTUwSEvSJBERs3ayrXMCS5GkPYJBWpImgYg4APheRLwyIpZFxPqI6IiIqyOiF7i6at9vln++PyJ+MuT2rEb9DJLUarwgiyRNApl5X0ScDjyUmUsiovpqZe8ELouIFwMfBxZHxDLgTcD8qv22ZOYvJ65qSWptjkhL0iQQEa8GngNkRPwEWFK1eQnweWAvYCnwA4orCx4ItANfKm/HT1zFktT6HJGWpMnhP4ErgIeAn2bmCyLiTOAZwPeA84H7y1vFNuAlQGU6x/SJK1eSWp9BWpImgczcGhEvB04G1pTN/00x8nwocA4wl2IEesfTgAuqjxMRizLz3roXLEmTgFM7JGnyeBmwP3BKRKwF7gAeAa4DVgNbqvZtA14PbAKOAfqAw4FnTmTBktTKDNKSNHmcCfx7Zr4AuJ5i6gYUK3a8DthMEZznACvKbW+imFv9euD5E1qtJLU4g7QkTQIRcTDQnpmPREQX0F9u6gEOAI4DlmfmJcCazHxbuf1M4BLgzylOOJQkjZJBWpImh5cCn42IpwE/Bb4ODFCMQN8DvI9i5HmoLwMvAD4J/NmEVCpJk0RkZqNrkCSNg4iI9C91SZowBmlJkiSpBk7tkCRJkmpgkJYkSZJqYJCWJEmSamCQliRJkmpgkJYkSZJqYJCWJEmSamCQliRJkmrw/wP5CFLKEXKKbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\u001b[1;31m2006-10-19\u001b[0m收盘到\u001b[1;92m2022-10-20\u001b[0m收盘A股涨跌幅#########################\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>涨幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1276.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.902938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.457987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.648029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.753522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.789876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.187665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>102.734871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                涨幅\n",
       "count  1276.000000\n",
       "mean      3.902938\n",
       "std       7.457987\n",
       "min      -0.648029\n",
       "25%       0.753522\n",
       "50%       1.789876\n",
       "75%       4.187665\n",
       "max     102.734871"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S6mCQliRJkurQshdkmTVrVi5cuLDZZUiSJGkcu/baa1dk5uzh1rVskF64cCGLFi1qdhmSJEkaxyLi/u2tc2iHJEmSVAeDtCRJklQHg7QkSZJUB4O0JEmSVAeDtCRJklQHg7QkSZJUB4O0JEmSVAeDtCRJklQHg7QkSZJUB4O0JEmSVAeDtCRJklSHtmYX0Gr+9M1ns2LV2l3eftb0KVxw3rkNrEiSJEnNYJDeTStWreWEMz+wy9v//IsfamA1kiRJahaHdkiSJEl1MEhLkiRJdTBIS5IkSXUwSEuSJEl1MEhLkiRJdTBIS5IkSXUwSEuSJEl1MEhLkiRJdTBIS5IkSXUwSEuSJEl1aGiQjoj2iNgQEdeXt9Mj4sCIuDYi7o6I99Zs+/5y2TURsbCRdUmSJEl7qq3B+58JXJ2ZLxxcEBEXAB8Fvg/8KiK+B2wATgcOA04CPgG8psG1SZIkSXVr9NCOGcCKIcuOAy7NzH7gkvL5CcBl5bLLgGc3uC5JkiRpjzQ6SHcAz4+IGyPiRxFxIDAxMzeX6x8B5gFzKAN3ZvYB1YioNrg2SZIkqW4NHdqRmddHxD6ZmRHxRuAcIIdsFttZ9jgRcRZwFsB+++030uVKkiRJu6zhs3Zk5mBI/m/gCGBdRHSVy+ZQ9EovAWYBRERb+bL+YfZ1fmYek5nHzJ49u9GlS5IkSdvV6Fk7ZtcM0XgxcD1wOXByufwU4IrydmK57CTgqkbWJUmSJO2pRs/acRTwmYjoAVYBZwLrgB8CnwQuyMzbASLiy8AdFDN4nNLguiRJkqQ90ugx0lcATxpm1bHDbHsOxRhqSZIkaczzyoaSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdDNKSJElSHQzSkiRJUh0M0pIkSVIdGh6kI2J2RKyIiOMj4sCIuDYi7o6I99Zs8/5y2TURsbDRNUmSJEl7ajR6pD8J3FI+fh/wUeAw4NSIOCIi9gdOL5d9CPjEKNQkSZIk7ZG2Ru48Ik4EltYsOg44OzP7I+KS8vkm4LJy2WXA5xtZkyRJkjQSGtYjHRETgfdQ9DIPmpiZm8vHjwDzgDnACoDM7AOqEVFtVF2SJEnSSGjk0I6PAP+SmRtrluWQbWI7y4YVEWdFxKKIWLR8+fIRKlOSJEnafY0c2nEacHpEAMwCTgTWRkRX2Ss9h6JXejXwRICIaAMyM/uH22Fmng+cD3DMMccMDeCSJEnSqGlYj3RmHpCZCzNzIfBt4LXA5cDJ5dCNU4ArytuJ5bKTgKsaVZMkSZI0Uhp6suEwPgj8kGImjwsy83aAiPgycAewgSJgS5IkSWPaqATpzHxTzdNjh1l/DnDOaNQiSZIkjQSvbChJkiTVwSAtSZIk1cEg3WADToktSZI0LhmkG+h3D6zilv3/kFsWr2l2KZIkSRphBukGWblhC//7+0fJShsf+uGtZDrttSRJ0nhikG6AgUwuv3UZ7ZVg7sqbuPrelVx609JmlyVJkqQRZJBugOsfXM3StZs57rDZzFl9C4fPm8zHLr2Nzb3DXrBRkiRJLcggPcJWbdzCr37/KAfOmshhcycTJB889Uk8vHoT5//inmaXJ0mSpBEy2lc2HPduW7KWgUxOOHwOEcENN9zAx/7ur5g657l86sd9/OiLH6dtoGe7r581fQoXnHfuKFYsSZKkehikR9iytT3MmtjJpM7iR9s7ACec+QGWrt3Mt655kP1ffjaHzp283df//IsfGq1SJUmStAcc2jGCMpNlazczd0rn49bNntRJWyVYsnpzEyqTJEnSSDNIj6DVm3rp6Rtg7tSux62rVoJ5U7pYvGZTEyqTJEnSSDNIj6Bla4ve5nlTHh+kAeZP62L5+h56+wdGsyxJkiQ1gEF6BC1b00N7NZgxsWPY9fOnTiBzW+CWJElS6zJIj6ClazczZ3IXlYhh188vh3wsXmOQliRJanUG6RHSP5AsX98z7ImGg7raq8zo7mDJasdJS5IktTqD9AhZsb6H/oHc7vjoQfOndbF0zWYyc5QqkyRJUiMYpEfI4LjnuTsL0lO72Nw3wKqNvaNRliRJkhrEID1Clq3tYUJ7lcldO77GzT5TJwA4DZ4kSVKLM0iPkMELscR2TjQcNK27na62ihdmkSRJanEG6RGwpW+ARzds2en4aICIYP60CSyxR1qSJKmlGaRHwCPryvHRw1zRcDjzp3axamMvm3r7G1mWJEmSGsggPQKWre0Bdn6i4aDBnutHvDCLJElSyzJIj4BVG7fQ3VFlQnt1l7afOam48uHKDVsaWZYkSZIayCA9AtZs6mXqhPZd3r67o40J7VWDtCRJUgszSI+A3Q3SADMmdvCoQVqSJKllGaT3UP9Asm5z324H6ekT21m5YYtXOJQkSWpRBuk9tHZzcYXC3Q3SMyd20tM3wMYtztwhSZLUigzSe2jNxvqC9IyJnnAoSZLUygzSe2jNJoO0JEnS3sggvYfWbOqlrRJ0d+za1HeDJnZU6ahWWLnRIC1JktSKDNJ7aHDGjojYrddFBDMmdtgjLUmS1KIM0nuonqnvBhmkJUmSWpdBeg9k5h4F6ZkTO9i4pZ/Nvc7cIUmS1GoM0ntg45Z++gay7iA93RMOJUmSWpZBeg+sHpyxo7v+HmkwSEuSJLUig/QeqHfqu0GTu9poq4SXCpckSWpBBuk9MBikp3TVF6SduUOSJKl1GaT3wJpNvUzuaqNa2b2p72oZpCVJklqTQXoPrNlY/4wdg2ZM7GB9Tx89fc7cIUmS1EoaGqQj4nkRcXVE3BgRv46IwyPiwIi4NiLujoj31mz7/nLZNRGxsJF1jZQ9mfpu0OClwldt6B2JkiRJkjRK2hq8/98DJ2fmioj4P8A7gSrwUeD7wK8i4nvABuB04DDgJOATwGsaXNse2dI3wKbe/pEL0hu3MG9q10iUJkmSpFHQ0B7pzFxShugAngjcAhwHXJqZ/cAl5fMTgMvKZZcBz25kXSNhT2fsGDSlq50IWL3RHmlJkqRWsktBOiI+PMyyj+zia98OLAaeAnwBmJiZm8vVjwDzgDnACoDM7AOqEVHdlf03y0gF6WolmNLVzqqNnnAoSZLUSnY4tCMiDqYYbvFHEfHrmlWTgT8C3rezA2TmZyPic8BfA+cCOfQw21k2XD1nAWcB7Lfffjs7dEONVJAGmN7dvvXiLpIkSWoNOxsjXQGeD0wFzqhZvgH4s109SGZmRHwduBJYFxFdZa/0HIpe6dUUQz+IiLbyJY+bxiIzzwfOBzjmmGOGhu9RtXZTL51tFbra97zjfFp3Bw+vXkNmU9+SJEmSdsMOg3Rm3gn8XURcnJm/2d2dR8ShwN2ZOQC8FLiZYgjHyRFxMXAK8CcUQfodEfEBipMNr9rdY422dT19TOoamXM1p3W309ufbNjiFHiSJEmtYleTYCUivgnMpmbYRWa+cCevOxE4OyI2AUsphmVsBn4IfBK4IDNvB4iILwN3UPR2n7I7b6IZ1m/uY3LnyATp6d3FzB2rHSctSZLUMnY1CV4A/C3FdHa7LDM/C3x2mFXHDrPtOcA5u7P/Zlrf08fcqZ0jsq9p5ThrZ+6QJElqHbsapNdn5g8aWkkL6esv5pCe3LnnJxoCWy8z7swdkiRJrWNXg/SvIuKHwM+AnsGFmXluQ6oa49b19AGM2BjpiGDahHZWb+xl0ojsUZIkSY22q0nw0fI2pYG1tIz1m4sgPVJjpKE44XDVBoO0JElSq9ilJJiZ/9ToQlrJ+hHukYZiCrx7V2xgwfBTaEuSJGmM2aUkGBE/5/EXTcnMfNHIlzT2bR3aMYI90tO72xlI2NLWPWL7lCRJUuPsahJ8W83jCcCLKK5uuFdav7mPrvYK7dVdusL6Lpk2oZgCb0v7XvtjlSRJaim7OrTjliGLFkXE9ezCJcLHo3Wbe0e0NxqKMdIAPQZpSZKklrCrQzvOHrLoIGCvnfR4fU8fk7tGZuq7Qd0dVTqqFXraPZ9TkiSpFezq2ITZNbdZFBdmeVmjihrr1m/uG/Ee6YhgWne7PdKSJEktYrdm7YiIeeXzpY0saiwbiCqb+wZGdMaOQdO621ltkJYkSWoJu9QjHRHHRsQtwPeBH0TEzRHxnMaWNjb1lrNqjOQc0oOmd3ewpa2bnr7+Ed+3JEmSRtaupsHPAC/NzIcAImIf4HvAsxpV2Fi1pVoG6Qb1SBMVHly5kYPn2DMtSZI0lu3qGOmOwRBdWgJ0NqCeMW+wR3qkx0hDcVEWgHuWbxjxfUuSJGlk7Woa/G5E/AT4CRDAHwAXNqyqMayRQXr6hGImkHtXGKQlSZLGuh32SEfEWyNiSmZ+GHg/xZR3PRTzR98zCvWNOb1t3Uxor9I2ghdjGdTZXqXav5n7HjVIS5IkjXU761Z9W2Z+HiAzrwauHlwRERcAFzWwtjGpt21CQ2bsGNTZu86hHZIkSS1gZ92q1R2s2zvHSFcnNmTGjkGdvesc2iFJktQCdhakr4uIPxu6MCJOBx5sTElj22j0SD+yrocNPX0NO4YkSZL23M4S4ZuB/46IvwCuBfqBJwNdwGmNLW3s2bilj/5qZ0NONBzU2bsWKE44PHLB1IYdR5IkSXtmh4kwM9cAL42Iw4HDKGbs+EJm3jQaxY01i1dvBhozh/Sgji3rAIO0JEnSWLerlwi/Hbi9wbWMeUvXlEG6s71hx+jsWw/AfY6TliRJGtNGfg63cWzxmk0ADR0jXcl+9pna5QmHkiRJY5xBejcM9khP7NzRZCZ77oDZE7nHIC1JkjSmGaR3w5I1m2jr20RbpbE/toUzJ9ojLUmSNMYZpHfDqUctYN7KGxp+nANmTWTNpl5WbdjS8GNJkiSpPgbp3fDsg2YyY/29DT/OgbMnAji8Q5IkaQwzSI9BC2cWQdrhHZIkSWOXQXoMesKMbqqVcAo8SZKkMcwgPQa1VyvsN6PbHmlJkqQxzCA9Ri2c2e0YaUmSpDHMID1GHTBrEvet2EBmNrsUSZIkDcMgPUYdMHsim3r7Wba2p9mlSJIkaRgG6THqwFmDU+Ctb3IlkiRJGo5BeoxaOMsp8CRJksYyg/QYNX9KF90dVe5aZo+0JEnSWGSQHqMqleCQOZO4+xGDtCRJ0lhkkB7DDp4zmbseWdfsMiRJkjQMg/QYdsjcSSxb28OaTb3NLkWSJElDGKTHsEPnTgLgbnulJUmSxhyD9Bh2yJzJANzpCYeSJEljTkODdETsGxGXRsStEfGLiNgnIg6MiGsj4u6IeG/Ntu8vl10TEQsbWVerWDBtAhPanblDkiRpLGp0j3QP8PHMPAK4CHgH8D7go8BhwKkRcURE7A+cXi77EPCJBtfVEiqV4OA5kzzhUJIkaQxqaJDOzOWZ+Yvy6X3AdOA44NLM7AcuKZ+fAFxWLrsMeHYj62olh8yZZI+0JEnSGDSaY6TPAH4MTMzMzeWyR4B5wBxgBUBm9gHViKgO3UFEnBURiyJi0fLly0ep7OY6ZO5klq7dzNrNztwhSZI0loxKkI6Ik4D9gG8DOXT1dpY9Tmaen5nHZOYxs2fPHvlCx6BD5hQzd9grLUmSNLY0PEhHxMHAx4E/yswE1kVEV7l6DkWv9BJgVrl9G5DlMI+93iFOgSdJkjQmNXrWjsnAhcAbM3NJufhy4ORy6MYpwBXl7cRy2UnAVY2sq5XsO72brvaKPdKSJEljTFuD9/824GDgyxEBsBF4BfBD4JPABZl5O0BEfBm4A9hAEbAFVCvBQbMncecjBmlJkqSxpKFBOjP/GfjnYVYdO8y25wDnNLKeVnXo3Mlcfc+jzS5DkiRJNbyyYQs4eM4kFq/ZzDpn7pAkSRozDNItYHDmjrsd3iFJkjRmGKRbwKFzJwNOgSdJkjSWGKRbwBNmdDOhvcptS9c2uxRJkiSVDNItoFoJnjh/Mrc8bJCWJEkaKwzSLeLIBVO5ZfEaBgaGXgRSkiRJzWCQbhFHLpjKhi393PfohmaXIkmSJAzSLePIfaYCcPNih3dIkiSNBQbpFnHI3El0VCvc/PCaZpciSZIkDNIto71a4fD5kw3SkiRJY4RBuoUcuWAqNz+8hkxPOJQkSWo2g3QLOXKfqazd3MdDqzY1uxRJkqS9nkG6hRy5YAqAwzskSZLGAIN0Czl07mTaKsFNBmlJkqSmM0i3kK72KofMnewUeJIkSWOAQbrFPHnBFG7xhENJkqSmM0i3mCMXTOXRDVtYunZzs0uRJEnaqxmkW8yTBq9w+LDDOyRJkprJIN1ijpg/hUrATQ+tbnYpkiRJezWDdIuZ0FHliH2mcM19q5pdiiRJ0l7NIN2CnrlwJtc9sIotfQPNLkWSJGmvZZBuQc88YAY9fQPc9PDqZpciSZK01zJIt6BnHjADgKvvXdnkSiRJkvZebc0uQI91ww03cOqr37DT7Tr3fRlf+O7P+dG5H2TW9ClccN65o1CdJEmSBhmkx5jeATjhzA/sdLu8/RHuWLqO405+P//zpQ+PQmWSJEmq5dCOFrVg2gS29A+wfH1Ps0uRJEnaKxmkW9SCaRMAeHj1piZXIkmStHcySLeoSV1tTJ3QzmKDtCRJUlMYpFvYgmkTeHj1JrLZhUiSJO2FDNItbMG0CWzuHaCnfUqzS5EkSdrrGKRb2ILpxTjpDV1zmlyJJEnS3scg3cKmdLUxqbONdd3zml2KJEnSXscg3cIiggNmTWTdhPls7u1vdjmSJEl7FYN0izto9kSy0savf/9os0uRJEnaqxikW9yC6ROoDPTyk1uXNbsUSZKkvYpBusW1VSpM3riEn962jIEBJ8KTJEkaLQbpcWDKxod5ZF0PNz68ptmlSJIk7TUM0uPA5I2LqVaCKxzeIUmSNGoM0uNA28AWnrFwOpcbpCVJkkZNw4N0RMyIiKsi4oPl8wMj4tqIuDsi3luz3fvLZddExMJG1zXevPiJc7lj2ToeeHRjs0uRJEnaKzQ0SEdEO3AZcGfN4vcBHwUOA06NiCMiYn/g9HLZh4BPNLKu8egPjpgLwOW32SstSZI0GhoapDOzFzgFuKpm8XHApZnZD1xSPj8BuKxcdhnw7EbWNR7tP3Mih8+bzCU3Lm52KZIkSXuFhg/tyMxHhiyamJmby8ePAPOAOcCKcvs+oBoR1UbXNt686un78rsHVnPnsnXNLkWSJGnca8bJhkMnO47tLHuciDgrIhZFxKLly5c3pLhWdvpTF9BeDb51zYPNLkWSJGnca0aQXhcRXeXjORS90kuAWQAR0QZkOczjMTLz/Mw8JjOPmT179qgV3CpmTurkD46Yy/d+9zBb+gaaXY4kSdK41owgfTlwcjl04xTgivJ2YrnsJB47plq74dXHPIGVG7ZwhScdSpIkNVQzgvQHgXcDdwE/yMzbM3Mp8GXgDuDDwN82oa5x4fmHzGafqV180+EdkiRJDdU2GgfJzK/UPF4OHDvMNucA54xGPeNZtRK86pgn8Lmf3cXDqzexYNqEZpckSZI0Lnllw3HojKfvC8BFi+yVliRJahSD9Dj0hBndvOCQ2Xz9Nw+wufdx52xKkiRpBBikx6mzjz+IFet7uPC3DzS7FEmSpHHJID1OPevAmTzzgBmc9z/30NNnr7QkSdJIM0iPY3/5woNZunYzFy16qNmlSJIkjTsG6XHseQfP4ugnTOMLV/6e3n4v0CJJkjSSDNLjWETw9hcdzMOrN/G93z3c7HIkSZLGFYP0OHfCYXM4csEUPnPFXWza4lhpSZKkkWKQHucigvedfAQPr97EuVfe3exyJEmSxg2D9F7g2ANnctrR+3De/9zDvSs2NLscSZKkcWFULhGuxrrhhhs49dVv2OE2vdUu+p5wMqd86EKe0XMdXz7v3FGqTpIkaXwySI8DvQNwwpkf2Ol2sx9YxS/u6uCetVNGoSpJkqTxzaEde5Gj9p3GzEkdPDzr6azcsKXZ5UiSJLU0g/RepFIJXnLEXPqrnfztt28kM5tdkiRJUssySO9l5kzuYt6jN3DFbcv46q/vb3Y5kiRJLcsgvReatfYOTjhsNh+99DZuXby22eVIkiS1JIP0XiiAfz3jKKZOaOet37iO1RsdLy1JkrS7DNJ7qZmTOvn8657Gw6s2cdbXrqWnz6seSpIk7Q6D9F7smQfM4JNnPIXf3ruSd190IwMDnnwoSZK0q5xHei/3iqMX8NCqTXzyx3ewYPoE3nPi4c0uSZIkqSUYpMXZxx/Ew6s38YUrf08A737pYUREs8uSJEka0wzSe6HhLimewIxZx3DulfCt71/KPo9ex2CUnjV9Chd4SXFJkqTHMEjvhbZ3SfHM5Kq7VvA7DmPu4c/ghYfPoVoJfv7FDzWhSkmSpLHNIK2tIoLnHzKL9rYKv713Jas2buGkI+c3uyxJkqQxyVk79BgRwbMPnMnLjpzHivU9fOO3D7C+a06zy5IkSRpzDNIa1qFzJ/PaZ+xHV3uFe+a/kPddfBNrNvY2uyxJkqQxwyCt7ZoxsYPXPmM/Zq29k29c/QAv/LcruWjRg/Q737QkSZJBWjvW0VZhn0ev44d/+Tz2m9nNu799Iy/8tyv52m/uZ3OvV0OUJEl7L4O0dsmT9pnKd97yHL7w+qcxrbuD9198M8/5+M/4wPdv5pr7VnpVREmStNdx1g7tskoleNmT53PikfP47b0r+eqv7+db1zzIV399P/OndnH8YXM47tDZPOfgmUzpam92uZIkSQ1lkNZuiwiedeBMnnXgTNb39HHFrcu49KYl/PCGxVz42weIHKB78wombVrC5I1LmbBlJTu6TqIXfJEkSa3IIK09MqmzjdOeuoDTnrqA3v4Brrt/FW/7xJeI/Z7GsnVzWDbjKCa0V9lvRjf7z+xmvxndTOx87MfOC75IkqRWZJDWTg13SfEdWX7zzfzVZ05j45Y+Hnh0I/ev3Mj9j27kjmXrAJg9uZP9Z3Rz0JxJzJ3c2aiyJUmSGsogrZ3a3iXFt2fR2a8EoLujjcPnT+Hw+VPITJav69kaqq97YBWL7l/FtAntdE47kntXbOCAWRMb9RYkSZJGnEFaoyIimDOlizlTunjGwhn09PZz1/L13LF0HQ9NP5IT/vVKjtp3Kq84egEvP2o+cyZ3NbtkSZKkHXL6OzVFZ3uVI/eZyiufti+HP/B93nvSE+kbSD50ya0c+7Gf8sdfuprvXPsQ63v6ml2qJEnSsOyRVtN19G/izBccyJkvOJC7H1nHxb9bzPdveJh3XnQD//C9m3jxEXM57egFHHfobDra/O4nSZLGBoO0xpSD50zmXS89jHe+5FCue2AV379+MZfcuIT/d+MSJrRXeeYBM3jewbN45gEzOHz+ZDrbqs0uWZIk7aUM0mq6nc0KsoBgyoR5rOveh99smMv/3LkcgPZqcOjcyRw+bwoHzOpm/5kT2Xf6BGZN6mTmpA66O/x4S5KkxjFpqOl2d1aQdZt7ueK7X+fk17yRmx9ewy/vXs53rut53HYT2qvMnNTBzEmdTO9uZ2JnG5M62ujurDKps43ujjYmdVbp7mhjYmcbEzurxX1H8XhwG4eTSJKk4YypIB0R7wfeCKwCzsjM+5pbkcaiyV3t3P+rS/jfDQ8CMBeYHVV62ifT29ZNX6WTvmoXfdUu1q/sZHW1i+iYwOz5C9jQ08fGnn7Wb+kjc9eOF9lPZaCPykAv1YE+KtlLZaCP6kAvlSyWT+qo8urTTmZSZxHKJw3euor7yV3F8u72KpXKjq7zKEmSWsWYCdIRsT9wOnAYcBLwCeA1TS1KY9bu9mJ/+m2v5ICjjmJS+TyBjCr9lXYGoo2BShsDlXb6y8cPLHmEF5xxFr39yZb+AXr7Bor7/gG29A1sXV48HmDFll4++eM7dl5IJpXspZ1+njBv9raQ3bEtdHd3VGmrVuioBu3VyuMet5ePKxG0VYLq9m6x7XFbJagM3kfQ0Vaho1op7tsqtFWCCAO+JEm7Y8wEaeAE4LLM7I+Iy4DPN7sgjR+7G7w/efYreep+79mt7d/xuYvKsJ1s6StC9mDY3npfPr7/lms5dO4BrNvcx/qePpat3cz6zX2s6+lj05Z++gZ2sbt8pGRSYYCJEzrpHBKyHxu6q3RUK8U25fL2tqCjWt26bWcZzKtlaC/uoVKG+8rW5dSs37ZttQJtlWIfbdUK1UrQXo3yvnxeqVCtBu2DXxSq245Zvh0GMssbZHk/uKx/IOnrL+8HBugrn/cNJP0DA1vX9dY87xvYtr63XD+QSSWCoHgvEcWc6ZXYznO2Pd/d7YbeZ5ZfCDPLe4As3/tjlyfFxrXPh76e2uVD17GtrkplsIayHoa8n8pj6x9s2+I9bnteKZ/X7qtSfpmrVLY9H2zDgYFtbTe4bOvjAegfbO+BwW0e296122Um/TWPKest6i7eU1ErUD6OodsMvvchX1qH+yJbrUC1Uik//8Xnu1KBasS2x0NeNx6/2GZmze9mcT/4PCnvB4b/3U22PR/8jD7mdbntMw1sbafttl+5HLZ9DoeuK1/+2Oflfga21lFbAzXLH1vzQM3v52Bdj/m95rG/I1vvB+uueV4pi9nh63nsvy2j0bbF/bY2eOy/TY/996X293Xwdzkzy+VDf9/Zum5KVzv7TJvQ8PezO8ZSkJ4DrADIzL6IqEZENTP7m1yXtEvayh7jXfHpz36Rh1b8duvzrvI2q3yeQFKBCAaiwq2338mffvj8rf/A9A/5R7v2fvA/l29//mOc/hd/P+w2/WXY6B/8h2oArv7J9+iYO4+BqLApqmyMCgNRJamQUSWjwkBse9zT109bR9fW51muk7TnKsHWQN3IGNSojFX7Zaw2NKs5thvEywC+9Qs3NWF4SAAeXFT7BXu023R+z4P8+lNvGd2D7kTkGPlkR8S7gb7M/FT5fDHwhNogHRFnAWeVTw8DduFv6Q03i/ILgMYV23X8sU3HJ9t1fLJdx6dWbdf9M3P2cCvGUo/0EuCJABHRBuTQ3ujMPB84vwm1bVdELMrMY5pdh0aW7Tr+2Kbjk+06Ptmu49N4bNexNK/XFcCJEVGlONnwqibXI0mSJG3XmOmRzsylEfFliuEaG4BTmlySJEmStF1jJkgDZOY5wDnNrmM3jamhJhoxtuv4Y5uOT7br+GS7jk/jrl3HzMmGkiRJUisZS2OkJUmSpJZhkN4DEfH+iLg7Iq6JiIXNrkf1iYh9I+LSiLg1In4REftExIERcW3Zvu9tdo2qT0TMjogVEXG8bTo+RMSTI+JnEXF9RPy57To+RMRnI+LGiFgUEcfYrq0rImZExFUR8cHy+bBtOV4ylEG6TkMuaf4hikuaqzX1AB/PzCOAi4B3AO8DPkrRvqdGxBFNrE/1+yRwS/nYNm1xEdEO/Dfwzsw8OjP/E9u15UXEM4EnZeZTgHdS/J9qu7ag8nf0MuDOmsWPa8vxlKEM0vXbeklzig/Ns5tcj+qUmcsz8xfl0/uA6cBxwKVl+15SPlcLiYgTgaXAveUi27T1HQ9cm5m/q1lmu7a+9cDEKK5lPRVYhe3akjKzl2LWtdopjIdry3GToQzS9XvMJc2BajkHtlrbGcCPgYmZublc9ggwr3klaXdFxETgPRQ9HYNs09Z3JNBfDsG6JiJegO3a8jLzVuBy4DfAPwDvwnZtWZn5yJBFw7XluMlQBun6DZ3uJJpShUZMRJwE7Ad8G9u31X0E+JfM3FizzDZtfZOA+cBLgD8BzsN2bXkRMQt4GfBZYCNF+9qu48dwbTlu2ndMzSPdYnZ6SXO1jog4GPg48NLMzIhYFxFd5bfoORTfotU6TgNOL/5SzCzgRGCtbdryVgOXl214W0RMBdbbri3vdcAlmflfEfED4DbAf4PHj+HacjXjJEPZI10/L2k+TkTEZOBC4I2ZuaRcfDlwctm+p1C0t1pEZh6QmQszcyHFXxhei206HvwIeHlEtEXEQcBK4CfYrq1uI9uGbswENuHv63gyXFuOmwxlj3SdvKT5uPI24GDgy2UP5kbgFcAPKWZ9uCAzb29eeRohH8Q2bWmZeVdEfBv4HdAPnE3Re2m7travU4SqGyj+5H82cD2263jxQYZpy/GSobyyoSRJklQHh3ZIkiRJdTBIS5IkSXUwSEuSJEl1MEhLkiRJdTBIS5IkSXUwSEvSOBER03awrn0US5GkvYJBWpLGgYjYH/hxRLw8IhZFxNrywiU/iogJFBczGdz22+X9uyLil0NuT23We5CkVuMFWSRpHMjM+yPiLGBxZh4TEbVXgnszcHFEvBD4F+DAiFgEvJ5tV5QD2JSZvxu9qiWptdkjLUnjQES8EngGkBHxS+CYmtXHAOcBM4BzgJ8BS4EDgCrwlfJ2wuhVLEmtzx5pSRof/h9wCbAY+FVmPi8i3gY8CfgxcC7wQHkb1AO8CBgczjF59MqVpNZnkJakcSAzN0fES4FTgBXl4v+l6Hk+HPgIMJuiB3rry4Dza/cTEQsz876GFyxJ44BDOyRp/HgJsB9wakSsAu4EHgWuApYDm2q2rQCvBTYAxwK9wJOBI0ezYElqZQZpSRo/3gb8V2Y+D7iaYugGFDN2vAbYSBGcZwFLynWvpxhb/VrguaNarSS1OIO0JI0DEXEwUM3MRyOiA+grV3UB+wPHAbdn5kXAisz8s3L924CLgHdQnHAoSdpFBmlJGh9eDHwhIg4FfgV8E+in6IG+B3gnRc/zUF8Fngd8GviLUalUksaJyMxm1yBJGgEREek/6pI0agzSkiRJUh0c2iFJkiTVwSAtSZIk1cEgLUmSJNXBIC1JkiTVwSAtSZIk1cEgLUmSJNXBIC1JkiTV4f8DQn0n9/mdIekAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\u001b[1;31m2005-10-19\u001b[0m收盘到\u001b[1;92m2022-10-20\u001b[0m收盘A股涨跌幅#########################\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>涨幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1246.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.262264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.299144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.244876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.935190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.602202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.729694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>165.791949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                涨幅\n",
       "count  1246.000000\n",
       "mean      7.262264\n",
       "std      13.299144\n",
       "min      -0.244876\n",
       "25%       1.935190\n",
       "50%       3.602202\n",
       "75%       7.729694\n",
       "max     165.791949"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\u001b[1;31m2004-10-19\u001b[0m收盘到\u001b[1;92m2022-10-20\u001b[0m收盘A股涨跌幅#########################\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>涨幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.233733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.529530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.510298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.198112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.652250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.218623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>222.050203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                涨幅\n",
       "count  1232.000000\n",
       "mean      6.233733\n",
       "std      14.529530\n",
       "min      -0.510298\n",
       "25%       1.198112\n",
       "50%       2.652250\n",
       "75%       6.218623\n",
       "max     222.050203"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\u001b[1;31m2003-10-19\u001b[0m收盘到\u001b[1;92m2022-10-20\u001b[0m收盘A股涨跌幅#########################\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>涨幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1124.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.411392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.754753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.678137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.815001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.926248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.632869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>480.851765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                涨幅\n",
       "count  1124.000000\n",
       "mean      5.411392\n",
       "std      18.754753\n",
       "min      -0.678137\n",
       "25%       0.815001\n",
       "50%       1.926248\n",
       "75%       4.632869\n",
       "max     480.851765"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\u001b[1;31m2002-10-19\u001b[0m收盘到\u001b[1;92m2022-10-20\u001b[0m收盘A股涨跌幅#########################\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>涨幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1059.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.829059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.270399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.815380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.321173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.131726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.085638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>423.973496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                涨幅\n",
       "count  1059.000000\n",
       "mean      3.829059\n",
       "std      16.270399\n",
       "min      -0.815380\n",
       "25%       0.321173\n",
       "50%       1.131726\n",
       "75%       3.085638\n",
       "max     423.973496"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "from text import *\n",
    "\n",
    "\n",
    "today = datetime.date.today()\n",
    "year = today.year\n",
    "if today.weekday() > 4:\n",
    "    n = 2\n",
    "else:\n",
    "    n = 1\n",
    "start = f\"{today.year-1}-12-31\"\n",
    "# start='2022-4-27'\n",
    "\n",
    "yesterday = get_previous_trading_date(today, n, market=\"cn\")\n",
    "if datetime.datetime.now().hour < 16:\n",
    "    today = yesterday\n",
    "\n",
    "starts = [\n",
    "    f\"{today.year-1}-12-31\",\n",
    "]\n",
    "\n",
    "\n",
    "for i in range(11, 21):\n",
    "    day = f\"{today.year-i}-{today.month}-{today.day}\"\n",
    "    day = datetime.datetime.strptime(day, \"%Y-%m-%d\")\n",
    "    day = day - datetime.timedelta(1)\n",
    "    day=f\"{day.year}-{day.month}-{day.day}\"\n",
    "    starts.append(day)\n",
    "df = all_instruments(type=\"CS\", market=\"cn\", date=today)\n",
    "index = df.order_book_id\n",
    "today = f\"{today.year}-{today.month}-{today.day}\"\n",
    "\n",
    "def get_df(start):\n",
    "    \"\"\"根据日期获取数据\"\"\"\n",
    "    df = get_price(\n",
    "        index,\n",
    "        start_date=start,\n",
    "        end_date=today,\n",
    "        frequency=\"1d\",\n",
    "        adjust_type=\"pre\",\n",
    "        skip_suspended=False,\n",
    "        market=\"cn\",\n",
    "        fields=\"close\",\n",
    "        expect_df=False,\n",
    "    )\n",
    "    datas = {\n",
    "        \"股票代码\": [],\n",
    "        \"股票名称\": [],\n",
    "        \"起始价格\": [],\n",
    "        \"结束价格\": [],\n",
    "        \"涨幅\": [],\n",
    "    }\n",
    "    for code in df.columns:\n",
    "        a = df[code][0]\n",
    "        b = df[code][-1]\n",
    "        if a > 0 and b > 0:\n",
    "            name = instruments(code).symbol\n",
    "            datas[\"股票代码\"].append(code)\n",
    "            datas[\"股票名称\"].append(name)\n",
    "            datas[\"起始价格\"].append(a)\n",
    "            datas[\"结束价格\"].append(b)\n",
    "            datas[\"涨幅\"].append(b / a - 1)\n",
    "    new_df = pd.DataFrame(datas)\n",
    "    filename = f\"csv/涨跌幅统计/{start}收盘到{today}收盘A股涨跌幅.csv\"\n",
    "    new_df.to_csv(filename)\n",
    "\n",
    "    title = f\"{OKRED}{start}{ENDL}收盘到{OKGREEN}{today}{ENDL}收盘A股涨跌幅\"\n",
    "    print(\"#\" * 25 + title + \"#\" * 25)\n",
    "    display(new_df[[\"涨幅\"]].describe())\n",
    "    fig = sns.histplot(new_df[\"涨幅\"], bins=50, kde=True)\n",
    "    # plt.gcf().set_size_inches(8, 2)#同时设置宽度和高度\n",
    "    fig.get_figure().set_figwidth(12)  # 设置宽度\n",
    "    fig.get_figure().set_figheight(5)  # 设置高度\n",
    "    plt.title(f\"{start}收盘到{today}收盘A股涨跌幅\")\n",
    "    plt.show()\n",
    "\n",
    "    data = [0, 0]\n",
    "    for d in new_df[\"涨幅\"]:\n",
    "        if d < 0:\n",
    "            data[0] += 1\n",
    "        else:\n",
    "            data[1] += 1\n",
    "    labels = [\"下跌\", \"上涨\"]\n",
    "    colors = sns.color_palette(\"pastel\")\n",
    "    plt.pie(data, labels=labels, colors=colors, autopct=\"%.2f%%\")\n",
    "    plt.gcf().set_size_inches(8, 8)\n",
    "    plt.title(f\"{start}收盘到{today}收盘A股涨跌幅\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "for start in starts:\n",
    "    get_df(start)\n"
   ]
  },
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   "cell_type": "code",
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  {
   "cell_type": "code",
   "execution_count": null,
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